Introduction
AI in 2026 is shifting from uncooked textual content mills to brokers that act and purpose. Specialists predict a give attention to sustained reasoning and multi-step planning in AI brokers. In observe, this implies LLMs should assume earlier than they converse, breaking duties into steps and verifying logic earlier than outputting solutions. Certainly, current analyses argue that 2026 shall be outlined by reasoning-first LLMs-models that deliberately use inside deliberation loops to enhance correctness. These fashions will energy autonomous brokers, self-debugging code assistants, strategic planners, and extra.
On the identical time, real-world AI deployment now calls for rigor: “the query is now not ‘Can AI do that?’ however ‘How properly, at what value, and for whom?’”. Thus, open fashions that ship high-quality reasoning and sensible effectivity are important.
Reasoning-centric LLMs matter as a result of many rising applications- from superior QA and coding to AI-driven research-require multi-turn logical chains. For instance, agentic workflows depend on fashions that may plan and confirm steps over lengthy contexts. Benchmarks of 2025 present that specialised reasoning fashions now rival proprietary methods on math, logic, and tool-using duties. Briefly, reasoning LLMs are the engines behind next-gen AI brokers and decision-makers.
On this weblog, we’ll discover the highest 10 open-source reasoning LLMs of 2026, their benchmark efficiency, architectural improvements, and deployment methods.
What Is a Reasoning LLM?
Reasoning LLMs are fashions tuned or designed to excel at multi-step, logic-driven duties (puzzles, superior math, iterative problem-solving) reasonably than one-shot Q&A. They usually generate intermediate steps or ideas of their outputs.
As an illustration, answering “If a practice goes 60 mph for 3 hours, how far?” requires computing distance = velocity×time earlier than answering-a easy reasoning activity. A real reasoning mannequin would explicitly embody the computation step in its response. Extra complicated duties equally demand chain-of-thought. In observe, reasoning LLMs typically have pondering mode: both they output their chain-of-thought in textual content, or they run hidden iterations of inference internally.
Fashionable reasoning fashions are these refined to excel at complicated duties finest solved with intermediate steps, equivalent to puzzles, math proofs, and coding challenges. They usually embody express reasoning content material within the response. Importantly, not all LLMs should be reasoning LLMs: less complicated duties like translation or trivia don’t require them. The truth is, utilizing a heavy reasoning mannequin in all places may be wasteful and even “overthinking.” The secret’s matching instruments to duties. However for superior agentic and STEM functions, these reasoning-specialist LLMs are important.
Architectural Patterns of Reasoning-First Fashions
Reasoning LLMs typically make use of specialised architectures and coaching:
- Combination of Specialists (MoE): Many high-end reasoning fashions use MoE to pack trillions of parameters whereas activating solely a fraction per token. For instance, Qwen3-Subsequent-80B prompts solely 3B parameters through 512 consultants, and GLM-4.7 is 355B whole with ~32B lively. Moonshot’s Kimi K2 makes use of ~1T whole parameters (32B lively) throughout 384 consultants. Nemotron 3 Nano (NVIDIA) makes use of ~31.6B whole (3.2B lively, through a hybrid MoE Transformer). MoE permits large mannequin capability for complicated reasoning with decrease per-token compute.
- Prolonged Context Home windows: Reasoning duties typically span lengthy dialogues or paperwork. Thus many fashions natively help large context sizes (128K-1M tokens). Kimi K2 and Qwen-coder fashions help 256K (extensible to 1M) contexts. LLaMA 3.3 extends to 128K tokens. Nemotron-3 helps as much as 1M context size. Lengthy context is essential for multi-step plan monitoring, software historical past, and doc understanding.
- Chain-of-Thought and Pondering Modes: Architecturally, reasoning LLMs typically have express “pondering” modes. For instance, Kimi K2 solely outputs in a “pondering” format with … blocks, imposing chain-of-thought. Qwen3-Subsequent-80B-Pondering routinely features a tag in its immediate to power reasoning mode. DeepSeek-V3.2 exposes an endpoint that by default produces an inside chain of thought earlier than last solutions. These modes may be toggled or managed at inference time, buying and selling off latency vs. reasoning depth.
- Coaching Strategies: Past structure, many reasoning fashions bear specialised coaching. OpenAI’s gpt-oss-120B and NVIDIA’s Nemotron all use RL from suggestions (typically with math/programming rewards) to spice up problem-solving. For instance, DeepSeek-R1 and R1-Zero have been educated with large-scale RL to straight optimize reasoning capabilities. Nemotron-3 was fine-tuned with a mixture of supervised fine-tuning (SFT) on reasoning information and multi-environment RL . Qwen3-Subsequent and GPT-OSS each undertake “pondering” coaching the place the mannequin is explicitly educated to generate reasoning steps. Such focused coaching yields markedly higher efficiency on reasoning benchmarks.
- Effectivity and Quantizations: To make these massive fashions sensible, many use aggressive quantization or distillation. Kimi K2 is natively INT4-quantized. Nemotron Nano was post-quantized to FP8 for quicker throughput. GPT-OSS-20B/120B are optimized to run on commodity GPUs. Moonshot’s MiniMax additionally emphasizes an “environment friendly design”: solely 10B activated parameters (with ~230B whole) to suit complicated agent duties.
Collectively, these patterns – MoE scaling, large contexts, chain-of-thought coaching, and cautious tuning – outline at this time’s reasoning LLM architectures.
1. GPT-OSS-120B
GPT-OSS-120B is a production-ready open-weight mannequin launched in 2025. It makes use of a Combination-of-Specialists (MoE) design with 117B whole / 5.1B lively parameters.
GPT-OSS-120B achieves near-parity with OpenAI’s o4-mini on core reasoning benchmarks, whereas working on a single 80GB GPU. It additionally outperforms different open fashions of comparable measurement on reasoning and gear use.
It additionally is available in a 20B model optimized for effectivity: the 20B mannequin matches o3-mini and may run on simply 16GB of RAM, making it superb for native or edge use. Each fashions help chain-of-thought with tags and full software integration through APIs. They help excessive instruction-following high quality and are absolutely Apache-2.0 licensed.
Key specs:
|
Variant
|
Complete Params
|
Lively Params
|
Min VRAM (quantized)
|
Goal {Hardware}
|
Latency Profile
|
|
gpt-oss-120B
|
117B
|
5.1B
|
80GB
|
1x H100/A100 80GB
|
180-220 t/s
|
|
gpt-oss-20B
|
21B
|
3.6B
|
16GB
|
RTX 4070/4060 Ti
|
45-55 t/s
|
Strengths and Limits
- Professionals: Close to-proprietary reasoning (AIME/GPQA parity), single-GPU viable, full CoT/software APIs for brokers.
- Cons: 120B deploy nonetheless wants tensor-parallel for <80GB setups; neighborhood fine-tunes nascent; no native picture/imaginative and prescient.
Optimized for latency
- GPT-OSS-120B can run on 1×A100/H100 (80GB), and OSS-20B on a 16GB GPU.
- Robust chain-of-thought & software use help.
2. GLM-4.7
GLM-4.7 is a 355B-parameter open mannequin with task-oriented reasoning enhancements. It was designed not only for Q&A however for end-to-end agentic coding and problem-solving. GLM-4.7 introduces “think-before-acting” and multi-turn reasoning controls to stabilize complicated duties. For instance, it implements “Interleaved Reasoning”, which means it performs a chain-of-thought earlier than each software name or response. It additionally has “Retention-Based mostly” and “Spherical-Degree” reasoning modes to maintain or skip inside monologue as wanted. These options let it adaptively commerce latency for accuracy.
Efficiency‑clever, GLM‑4.7 leads open-source fashions throughout reasoning, coding, and agent duties. On the Humanity’s Final Examination (HLE) benchmark with software use, it scores ~42.8 %, a big enchancment over GLM‑4.6 and aggressive with different high-performing open fashions. In coding, GLM‑4.7 achieves ~84.9 % on LiveCodeBench v6 and ~73.8 % on SWE-Bench Verified, surpassing earlier GLM releases.
The mannequin additionally demonstrates strong agent functionality on benchmarks equivalent to BrowseComp and τ²‑Bench, showcasing multi-step reasoning and gear integration. Collectively, these outcomes replicate GLM-4.7’s broad functionality throughout logic, coding, and agent workflows, in an open-weight mannequin launched below the MIT license.
Key Specs
- Structure: Sparse Combination-of-Specialists
- Complete parameters: ~355B (reported)
- Lively parameters: ~32B per token (reported)
- Context size: As much as ~200K tokens
- Major use instances: Coding, math reasoning, agent workflows
- Availability: Open-weight; business use permitted (license varies by launch)
Strengths
- Robust efficiency in multi-step reasoning and coding
- Designed for agent-style execution loops
- Lengthy-context help for complicated duties
- Aggressive with main open reasoning fashions
Weaknesses
- Excessive inference value on account of scale
- Superior reasoning will increase latency
- Restricted English-first documentation
3. Kimi K2 Pondering
Kimi K2 Pondering is a trillion-parameter Combination-of-Specialists mannequin designed particularly for deep reasoning and gear use. It options roughly 1 trillion whole parameters however prompts solely 32 billion per token throughout 384 consultants. The mannequin helps a local context window of 256K tokens, which extends to 1 million tokens utilizing Yarn. Kimi K2 was educated in INT4 precision, delivering as much as 2x quicker inference speeds.
The structure is absolutely agentic and all the time thinks first. Based on the mannequin card, Kimi K2-Pondering solely helps pondering mode, the place the system immediate routinely inserts a tag. Each output contains inside reasoning content material by default.
Kimi K2 Pondering leads throughout the proven benchmarks, scoring 44.9% on Humanity’s Final Examination, 60.2% on BrowseComp, and 56.3% on Seal-0 for real-world data assortment. It additionally performs strongly in agentic coding and multilingual duties, reaching 61.1% on SWE-Multilingual, 71.3% on SWE-bench Verified, and 83.1% on LiveCodeBench V6.
Total, these outcomes present Kimi K2 Pondering outperforming GPT-5 and Claude Sonnet 4.5 throughout reasoning, agentic, and coding evaluations.
Key Specs
- Structure: Massive-scale MoE
- Complete parameters: ~1T (reported)
- Lively parameters: ~32B per token
- Specialists: 384
- Context size: 256K (as much as ~1M with scaling)
- Major use instances: Deep reasoning, planning, long-context brokers
- Availability: Open-weight; business use permitted
Strengths
- Glorious long-horizon reasoning
- Very massive context window
- Robust tool-use and planning functionality
- Environment friendly inference relative to whole measurement
Weaknesses:
- Actually monumental scale (1T) means daunting coaching/inference overhead.
- Nonetheless early (new launch), so real-world adoption/tooling is nascent.
4. MiniMax-M2.1
MiniMax-M2.1 is one other agentic LLM geared towards tool-interactive reasoning. It makes use of a 230B whole param design with solely 10B activated per token, implying a big MoE or comparable sparsity.
The mannequin helps interleaved reasoning and motion, permitting it to purpose, name instruments, and react to observations throughout prolonged agent loops. This makes it well-suited for duties involving lengthy sequences of actions, equivalent to net navigation, multi-file coding, or structured analysis duties.
MiniMax stories sturdy inside outcomes on agent benchmarks equivalent to SWE-Bench, BrowseComp, and xBench. In observe, M2.1 is usually paired with inference engines like vLLM to help perform calling and multi-turn agent execution.
Key Specs
- Structure: Sparse, agent-optimized LLM
- Complete parameters: ~230B (reported)
- Lively parameters: ~10B per token
- Context size: Lengthy context (actual measurement not publicly specified)
- Major use instances: Software-based brokers, lengthy workflows
- Availability: Open-weight (license particulars restricted)
Strengths
- Goal-built for agent workflows
- Excessive reasoning effectivity per lively parameter
- Robust long-horizon activity dealing with
Weaknesses
- Restricted public benchmarks and documentation
- Smaller ecosystem than friends
- Requires optimized inference setup
5. DeepSeek-R1-Distill-Qwen3-8B
DeepSeek-R1-Distill-Qwen3-8B represents one of the spectacular achievements in environment friendly reasoning fashions. Launched in Could 2025 as a part of the DeepSeek-R1-0528 replace, this 8-billion parameter mannequin demonstrates that superior reasoning capabilities may be efficiently distilled from large fashions into compact, accessible codecs with out important efficiency degradation.
The mannequin was created by distilling chain-of-thought reasoning patterns from the complete 671B parameter DeepSeek-R1-0528 mannequin and making use of them to fine-tune Alibaba’s Qwen3-8B base mannequin. This distillation course of used roughly 800,000 high-quality reasoning samples generated by the complete R1 mannequin, specializing in mathematical problem-solving, logical inference, and structured reasoning duties. The result’s a mannequin that achieves state-of-the-art efficiency amongst 8B-class fashions whereas requiring solely a single GPU to run.
Efficiency-wise, DeepSeek-R1-Distill-Qwen3-8B delivers outcomes that defy its compact measurement. It outperforms Google’s Gemini 2.5 Flash on AIME 2025 mathematical reasoning duties and almost matches Microsoft’s Phi 4 reasoning mannequin on HMMT benchmarks. Maybe most remarkably, this 8B mannequin matches the efficiency of Qwen3-235B-Pondering on sure reasoning duties—a 235B parameter mannequin. The R1-0528 replace considerably improved reasoning depth, with accuracy on AIME 2025 leaping from 70% to 87.5% in comparison with the unique R1 launch.
The mannequin runs effectively on a single GPU with 40-80GB VRAM (equivalent to an NVIDIA H100 or A100), making it accessible to particular person researchers, small groups, and organizations with out large compute infrastructure. It helps the identical superior options as the complete R1-0528 mannequin, together with system prompts, JSON output, and performance calling—capabilities that make it sensible for manufacturing functions requiring structured reasoning and gear integration.
Key Specs
- Mannequin sort: Distilled reasoning mannequin
- Base structure: Qwen3-8B (dense transformer)
- Complete parameters: 8B
- Coaching method: Distillation from DeepSeek-R1-0528 (671B) utilizing 800K reasoning samples
- {Hardware} necessities: Single GPU with 40-80GB VRAM
- License: MIT License (absolutely permissive for business use)
- Major use instances: Mathematical reasoning, logical inference, coding help, resource-constrained deployments
Strengths
- Distinctive performance-to-size ratio: matches 235B fashions on particular reasoning duties at 8B measurement
- Runs effectively on single consumer-grade GPU, dramatically reducing deployment obstacles
- Outperforms a lot bigger fashions like Gemini 2.5 Flash on mathematical reasoning
- Totally open-source with permissive MIT licensing allows unrestricted business use
- Helps fashionable options: system prompts, JSON output, perform calling for manufacturing integration
- Demonstrates profitable distillation of superior reasoning from large fashions to compact codecs
Weaknesses
- Whereas spectacular for its measurement, nonetheless trails the complete 671B R1 mannequin on probably the most complicated reasoning duties
- 8B parameter restrict constrains multilingual capabilities and broad area data
- Requires particular inference configurations (temperature 0.6 really helpful) for optimum efficiency
- Nonetheless comparatively new (Could 2025 launch) with restricted manufacturing battle-testing in comparison with extra established fashions
6. DeepSeek-V3.2 Terminus
DeepSeek’s V3 collection (codename “Terminus”) builds on the R1 fashions and is designed for agentic AI workloads. It makes use of a Combination-of-Specialists transformer with ~671B whole parameters and ~37B lively parameters per token.
DeepSeek-V3.2 introduces a Sparse Consideration structure for long-context scaling. It replaces full consideration with an indexer-selector mechanism, decreasing quadratic consideration value whereas sustaining accuracy near dense consideration.
As proven within the under determine, the eye layer combines Multi-Question Consideration, a Lightning Indexer, and a High-Okay Selector. The indexer identifies related tokens, and a spotlight is computed solely over the chosen subset, with RoPE utilized for positional encoding.
The mannequin is educated with large-scale reinforcement studying on duties equivalent to math, coding, logic, and gear use. These abilities are built-in right into a shared mannequin utilizing Group Relative Coverage Optimization.
Fig- Consideration-architecture of deepseek-v3.2
DeepSeek stories that V3.2 achieves reasoning efficiency akin to main proprietary fashions on public benchmarks. The V3.2-Speciale variant is additional optimized for deep multi-step reasoning.
DeepSeek-V3.2 is MIT-licensed, accessible through manufacturing APIs, and outperforms V3.1 on combined reasoning and agent duties.
Key specs
- Structure: MoE transformer with DeepSeek Sparse Consideration
- Complete parameters: ~671B (MoE capability)
- Lively parameters: ~37B per token
- Context size: Helps prolonged contexts as much as ~1M tokens with sparse consideration
- License: MIT (open-weight)
- Availability: Open weights + manufacturing API through DeepSeek.ai
Strengths
- State-of-the-art open reasoning: DeepSeek-V3.2 constantly ranks on the high of open-source reasoning and agent duties.
- Environment friendly long-context inference: DeepSeek Sparse Consideration (DSA) reduces value progress on very lengthy sequences relative to straightforward dense consideration with out considerably hurting accuracy.
- Agent integration: Constructed-in help for pondering modes and mixed software/chain-of-thought workflows makes it well-suited for autonomous methods.
- Open ecosystem: MIT license and API entry through net/app ecosystem encourage adoption and experimentation.
Weaknesses
- Massive compute footprint: Regardless of sparse inference financial savings, the general mannequin measurement and coaching value stay important for self-hosting.
- Complicated tooling: Superior pondering modes and full agent workflows require experience to combine successfully.
- New launch: As a comparatively current era, broader neighborhood benchmarks and tooling help proceed to mature.
7. Qwen3-Subsequent-80B-A3B
Qwen3-Subsequent is Alibaba’s next-gen open mannequin collection emphasizing each scale and effectivity. The 80B-A3B-Pondering variant is specifically designed for complicated reasoning: it combines hybrid consideration (linearized + sparse mechanisms) with a high-sparsity MoE. Its specs are placing: 80B whole parameters, however solely ~3B lively (512 consultants with 10 lively). This yields very quick inference. Qwen3-Subsequent additionally makes use of multi-token prediction (MTP) throughout coaching for velocity.
Benchmarks present Qwen3-Subsequent-80B performing excellently on multi-hop duties. The mannequin card highlights that it outperforms earlier Qwen-30B and Qwen-32B pondering fashions, and even outperforms the proprietary Gemini-2.5-Flash on a number of benchmarks. For instance, it will get ~87.8% on AIME25 (math) and ~73.9% on HMMT25, higher than Gemini-2.5-Flash’s 72.0% and 73.9% respectively. It additionally reveals sturdy efficiency on MMLU and coding exams.
Key specs: 80B whole, 3B lively. 48 layers, hybrid structure with 262K native context. Totally Apache-2.0 licensed.
Strengths: Glorious reasoning & coding efficiency per compute (beats bigger fashions on many duties); large context; extraordinarily environment friendly (10× velocity up for >32K context vs older Qwens).
Weaknesses: As a MoE mannequin, it might require particular runtime help; “Pondering” mode provides complexity (all the time generates a block and requires particular prompting).
8. Qwen3-235B-A22B
Qwen3-235B-A22B represents Alibaba’s most superior open reasoning mannequin thus far. It makes use of an enormous Combination-of-Specialists structure with 235 billion whole parameters however prompts solely 22 billion per token, reaching an optimum steadiness between functionality and effectivity. The mannequin employs the identical hybrid consideration mechanism as Qwen3-Subsequent-80B (combining linearized and sparse consideration) however scales it to deal with much more complicated reasoning chains.
The “A22B” designation refers to its 22B lively parameters throughout a extremely sparse skilled system. This design permits the mannequin to take care of reasoning high quality akin to a lot bigger dense fashions whereas maintaining inference prices manageable. Qwen3-235B-A22B helps dual-mode operation: it could possibly run in customary mode for fast responses or swap to “pondering mode” with express chain-of-thought reasoning for complicated duties.
Efficiency-wise, Qwen3-235B-A22B excels throughout mathematical reasoning, coding, and multi-step logical duties. On AIME 2025, it achieves roughly 89.2%, outperforming many proprietary fashions. It scores 76.8% on HMMT25 and maintains sturdy efficiency on MMLU-Professional (78.4%) and coding benchmarks like HumanEval (91.5%). The mannequin’s long-context functionality extends to 262K tokens natively, with optimized dealing with for prolonged reasoning chains.
The structure incorporates multi-token prediction throughout coaching, which improves each coaching effectivity and the mannequin’s capability to anticipate reasoning paths. This makes it notably efficient for duties requiring ahead planning, equivalent to complicated mathematical proofs or multi-file code refactoring.
Key Specs
- Structure: Hybrid MoE with dual-mode (customary/pondering) operation
- Complete parameters: ~235B
- Lively parameters: ~22B per token
- Context size: 262K tokens native
- License: Apache-2.0
- Major use instances: Superior mathematical reasoning, complicated coding duties, multi-step drawback fixing, long-context evaluation
Strengths
- Distinctive mathematical and logical reasoning efficiency, surpassing many bigger fashions
- Twin-mode operation permits flexibility between velocity and reasoning depth
- Extremely environment friendly inference relative to reasoning functionality (22B lively vs. 235B whole)
- Native long-context help with out requiring extensions or particular configurations
- Complete Apache-2.0 licensing allows business deployment
Weaknesses
- Requires MoE-aware inference runtime (vLLM, DeepSpeed, or comparable)
- Pondering mode provides latency and token overhead for easy queries
- Much less mature ecosystem in comparison with LLaMA or GPT variants
- Documentation primarily in Chinese language, with English supplies nonetheless growing
9. MiMo-V2-Flash
MiMo-V2-Flash represents an aggressive push towards ultra-efficient reasoning by a 309 billion parameter Combination-of-Specialists structure that prompts solely 15 billion parameters per token. This 20:1 sparsity ratio is among the many highest in manufacturing reasoning fashions, enabling inference speeds of roughly 150 tokens per second whereas sustaining aggressive efficiency on mathematical and coding benchmarks.
The mannequin makes use of a sparse gating mechanism that dynamically routes tokens to specialised skilled networks. This structure permits MiMo-V2-Flash to attain exceptional value effectivity, working at simply 2.5% of Claude’s inference value whereas delivering comparable efficiency on particular reasoning duties. The mannequin was educated with a give attention to mathematical reasoning, coding, and structured problem-solving.
MiMo-V2-Flash delivers spectacular benchmark outcomes, reaching 94.1% on AIME 2025, putting it among the many high performers for mathematical reasoning. In coding duties, it scores 73.4% on SWE-Bench Verified and demonstrates sturdy efficiency on customary programming benchmarks. The mannequin helps a 128K token context window and is launched below an open license allowing business use.
Nevertheless, real-world efficiency reveals some limitations. Group testing signifies that whereas MiMo-V2-Flash excels on mathematical and coding benchmarks, it could possibly battle with instruction following and general-purpose duties exterior its core coaching distribution. The mannequin performs finest when duties carefully match mathematical competitions or coding challenges however reveals inconsistent high quality on open-ended reasoning duties.
Key Specs
- Structure: Extremely-sparse MoE (309B whole, 15B lively)
- Complete parameters: ~309B
- Lively parameters: ~15B per token (20:1 sparsity)
- Context size: 128K tokens
- License: Open-weight, business use permitted
- Inference velocity: ~150 tokens/second
- Major use instances: Mathematical competitions, coding challenges, cost-sensitive deployments
Strengths
- Distinctive effectivity with 15B lively parameters delivering sturdy math and coding efficiency
- Excellent value profile at 2.5% of Claude’s inference value
- Quick inference at 150 t/s allows real-time functions
- Robust mathematical reasoning with 94.1% AIME 2025 rating
- Latest launch represents cutting-edge MoE effectivity strategies
Weaknesses
- Instruction-following may be inconsistent on general-purpose duties
- Efficiency is strongest inside math and coding domains, much less dependable on various workloads
- Restricted ecosystem maturity with sparse neighborhood tooling and documentation
- Finest fitted to slim, well-defined use instances reasonably than basic reasoning brokers
10. Ministral 14B Reasoning
Mistral AI’s Ministral 14B Reasoning represents a breakthrough in compact reasoning fashions. With solely 14 billion parameters, it achieves reasoning efficiency that rivals fashions 5-10× its measurement, making it probably the most environment friendly mannequin on this top-10 listing. Ministral 14B is a part of the broader Mistral 3 household and inherits architectural improvements from Mistral Massive 3 whereas optimizing for deployment in resource-constrained environments.
The mannequin employs a dense transformer structure with specialised reasoning coaching. Not like bigger MoE fashions, Ministral achieves its effectivity by cautious dataset curation and reinforcement studying targeted particularly on mathematical and logical reasoning duties. This focused method permits it to punch properly above its weight class on reasoning benchmarks.
Remarkably, Ministral 14B achieves roughly 85% accuracy on AIME 2025, a number one end result for any mannequin below 30B parameters and aggressive with fashions a number of instances bigger. It additionally scores 68.2% on GPQA Diamond and 82.7% on MATH-500, demonstrating broad reasoning functionality throughout totally different drawback varieties. On coding benchmarks, it achieves 78.5% on HumanEval, making it appropriate for AI-assisted improvement workflows.
The mannequin’s small measurement allows deployment eventualities unattainable for bigger fashions. It might probably run successfully on a single client GPU (RTX 4090, A6000) with 24GB VRAM, and even on high-end laptops with quantization. Inference speeds attain 40-60 tokens per second on client {hardware}, making it sensible for real-time interactive functions. This accessibility opens reasoning-first AI to a wider vary of builders and use instances.
Key Specs
- Structure: Dense transformer with reasoning-optimized coaching
- Complete parameters: ~14B
- Lively parameters: ~14B (dense)
- Context size: 128K tokens
- License: Apache-2.0
- Major use instances: Edge reasoning, native improvement, resource-constrained environments, real-time interactive AI
Strengths
- Distinctive reasoning efficiency relative to mannequin measurement (~85% AIME 2025 at 14B)
- Runs on client {hardware} (single RTX 4090 or comparable) with sturdy efficiency
- Quick inference speeds (40-60 t/s) allow real-time interactive functions
- Decrease operational prices make reasoning AI accessible to smaller groups and particular person builders
- Apache-2.0 license with minimal deployment obstacles
Weaknesses
- Decrease absolute ceiling than 100B+ fashions on probably the most tough reasoning duties
- Restricted context window (128K) in comparison with million-token fashions
- Dense structure means no parameter effectivity beneficial properties from sparsity
- Could battle with extraordinarily lengthy reasoning chains that require sustained computation
- Smaller mannequin capability limits multilingual and multimodal capabilities
Mannequin Comparability Abstract
|
Mannequin
|
Structure
|
Params (Complete / Lively)
|
Context Size
|
License
|
Notable Strengths
|
|
GPT-OSS-120B
|
Sparse / MoE-style
|
~117B / ~5.1B
|
~128K
|
Apache-2.0
|
Environment friendly GPT-level reasoning; single-GPU feasibility; agent-friendly
|
|
GLM-4.7 (Zhipu AI)
|
MoE Transformer
|
~355B / ~32B
|
~200K enter / 128K output
|
MIT
|
Robust open coding + math reasoning; built-in software & agent APIs
|
|
Kimi K2 Pondering (Moonshot AI)
|
MoE (≈384 consultants)
|
~1T / ~32B
|
256K (as much as 1M through Yarn)
|
Apache-2.0
|
Distinctive deep reasoning and long-horizon software use; INT4 effectivity
|
|
MiniMax-M2.1
|
MoE (agent-optimized)
|
~230B / ~10B
|
Lengthy (not publicly specified)
|
MIT
|
Engineered for agentic workflows; sturdy long-horizon reasoning
|
|
DeepSeek-R1 (distilled)
|
Dense Transformer (distilled)
|
8B / 8B
|
128K
|
MIT
|
Matches 235B fashions on reasoning; runs on single GPU; 87.5% AIME 2025
|
|
DeepSeek-V3.2 (Terminus)
|
MoE + Sparse Consideration
|
~671B / ~37B
|
As much as ~1M (sparse)
|
MIT
|
State-of-the-art open agentic reasoning; long-context effectivity
|
|
Qwen3-Subsequent-80B-Pondering
|
Hybrid MoE + hybrid consideration
|
80B / ~3B
|
~262K native
|
Apache-2.0
|
Extraordinarily compute-efficient reasoning; sturdy math & coding
|
|
Qwen3-235B-A22B
|
Hybrid MoE + dual-mode
|
~235B / ~22B
|
~262K native
|
Apache-2.0
|
Distinctive math reasoning (89.2% AIME); dual-mode flexibility
|
|
Ministral 14B Reasoning
|
Dense Transformer
|
~14B / ~14B
|
128K
|
Apache-2.0
|
Finest-in-class effectivity; 85% AIME at 14B; runs on client GPUs
|
|
MiMo-V2-Flash
|
Extremely-sparse MoE
|
~309B / ~15B
|
128K
|
MIT
|
Extremely-efficient (2.5% Claude value); 150 t/s; 94.1% AIME 2025
|
Conclusion
Open-source reasoning fashions have superior shortly, however working them effectively stays an actual problem. Agentic and reasoning workloads are basically token-intensive. They contain lengthy contexts, multi-step planning, repeated software calls, and iterative execution. Consequently, they burn by tokens quickly and grow to be costly and gradual when run on customary inference setups.
The Clarifai Reasoning Engine is constructed particularly to handle this drawback. It’s optimized for agentic and reasoning workloads, utilizing optimized kernels and adaptive strategies that enhance throughput and latency over time with out compromising accuracy. Mixed with Compute Orchestration, Clarifai dynamically manages how these workloads run throughout GPUs, enabling excessive throughput, low latency, and predictable prices at the same time as reasoning depth will increase.
These optimizations are mirrored in actual benchmarks. In evaluations printed by Synthetic Evaluation on GPT-OSS-120B, Clarifai achieved industry-leading outcomes, exceeding 500 tokens per second with a time to first token of round 0.3 seconds. The outcomes spotlight how execution and orchestration decisions straight impression the viability of enormous reasoning fashions in manufacturing.
In parallel, the platform continues so as to add and replace help for high open-source reasoning fashions within the neighborhood. You can strive these fashions straight within the Playground or entry them by the API and combine them into their very own functions. The identical infrastructure additionally helps deploying customized or self-hosted fashions, making it straightforward to judge, evaluate, and run reasoning workloads below constant circumstances.
As reasoning fashions proceed to evolve in 2026, the power to run them effectively and affordably would be the actual differentiator.
