Month: January 2026
xAI: Grok 4
xAI: Grok 4
Grok 4 is xAI’s latest reasoning model, featuring a 256K context window with support for parallel tool calling, structured outputs, and multimodal inputs (text and images). Unlike some models, its reasoning process is not exposed, cannot be disabled, and does not allow users to specify reasoning depth. Pricing tiers adjust once a request exceeds 128K total tokens.
| Creator | xAI |
| Release Date | July, 2025 |
| License | Proprietary |
| Context Window | 256,000 |
| Image Input Support | Yes |
| Open Source (Weights) | No |
| Input Cost | $3/M tokens |
| Output Cost | $15/M tokens |
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MiniMax – ChatSonic
Meta: Llama 4 Maverick
Meta: Llama 4 Maverick
Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal model from Meta, built on a Mixture-of-Experts (MoE) architecture with 128 experts and 17B active parameters per forward pass (400B total). It supports multilingual text and image inputs and generates text and code outputs across 12 languages. Instruction-tuned for assistant-like interaction, it excels in vision-language tasks, image reasoning, and general-purpose multimodal applications.
Maverick introduces early fusion for native multimodality and supports a 1M-token context window. Trained on ~22T tokens from public, licensed, and Meta-platform data, it has a knowledge cutoff of August 2024. Released on April 5, 2025 under the Llama 4 Community License, Maverick is designed for both research and commercial use cases that demand advanced multimodal reasoning and high throughput.
| Creator | Meta |
| Release Date | April, 2025 |
| License | Llama 4 Comunity Lisense Agreement |
| Context Window | 128,000 |
| Image Input Support | Yes |
| Open Source (Weights) | Yes |
| Parameters | 402B, 17B active at inference time |
| Model Weights | Click here |
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MistralAI – ChatSonic
MoonshotAI: Kimi K2 0711
MoonshotAI: Kimi K2 0711
Kimi K2 7011 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion parameters with 32B active per forward pass. Optimized for agentic tasks, it delivers advanced capabilities in reasoning, tool use, and code synthesis. The model achieves strong results across benchmarks, excelling in coding (LiveCodeBench, SWE-bench), reasoning (ZebraLogic, GPQA), and tool-use (Tau2, AceBench). With support for long-context inference up to 128K tokens, Kimi K2 leverages a novel training stack that includes the MuonClip optimizer for stable, large-scale MoE training.
| Creator | Moonshot AI |
| Release Date | July, 2025 |
| License | Modified MIT License |
| Context Window | 32,768 |
| Image Input Support | No |
| Open Source (Weights) | Yes |
| Parameters | 1000B, 32B active at inference time |
| Model Weights | Click here |
Performance Benchmarks
| Benchmark | Metric | Kimi K2 Instruct | DeepSeek-V3-0324 | Qwen3-235B-A22B (non-thinking) |
Claude Sonnet 4 (w/o extended thinking) |
Claude Opus 4 (w/o extended thinking) |
GPT-4.1 | Gemini 2.5 Flash Preview (05-20) |
|---|---|---|---|---|---|---|---|---|
| Coding Tasks | ||||||||
| LiveCodeBench v6 (Aug 24 – May 25) |
Pass@1 | 53.7 | 46.9 | 37.0 | 48.5 | 47.4 | 44.7 | 44.7 |
| OJBench | Pass@1 | 27.1 | 24.0 | 11.3 | 15.3 | 19.6 | 19.5 | 19.5 |
| MultiPL-E | Pass@1 | 85.7 | 83.1 | 78.2 | 88.6 | 89.6 | 86.7 | 85.6 |
| SWE-bench Verified (Agentless Coding) |
Single Patch w/o Test (Acc) | 51.8 | 36.6 | 39.4 | 50.2 | 53.0 | 40.8 | 32.6 |
| SWE-bench Verified (Agentic Coding) |
Single Attempt (Acc) | 65.8 | 38.8 | 34.4 | 72.7* | 72.5* | 54.6 | — |
| Multiple Attempts (Acc) | 71.6 | — | — | 80.2 | 79.4* | — | — | |
| SWE-bench Multilingual (Agentic Coding) |
Single Attempt (Acc) | 47.3 | 25.8 | 20.9 | 51.0 | — | 31.5 | — |
| TerminalBench | Inhouse Framework (Acc) | 30.0 | — | — | 35.5 | 43.2 | 8.3 | — |
| Terminus (Acc) | 25.0 | 16.3 | 6.6 | — | — | 30.3 | 16.8 | |
| Aider-Polyglot | Acc | 60.0 | 55.1 | 61.8 | 56.4 | 70.7 | 52.4 | 44.0 |
| Tool Use Tasks | ||||||||
| Tau2 retail | Avg@4 | 70.6 | 69.1 | 57.0 | 75.0 | 81.8 | 74.8 | 64.3 |
| Tau2 airline | Avg@4 | 56.5 | 39.0 | 26.5 | 55.5 | 60.0 | 54.5 | 42.5 |
| Tau2 telecom | Avg@4 | 65.8 | 32.5 | 22.1 | 45.2 | 57.0 | 38.6 | 16.9 |
| AceBench | Acc | 76.5 | 72.7 | 70.5 | 76.2 | 75.6 | 80.1 | 74.5 |
| Math & STEM Tasks | ||||||||
| AIME 2024 | Avg@64 | 69.6 | 59.4* | 40.1* | 43.4 | 48.2 | 46.5 | 61.3 |
| AIME 2025 | Avg@64 | 49.5 | 46.7 | 24.7* | 33.1* | 33.9* | 37.0 | 46.6 |
| MATH-500 | Acc | 97.4 | 94.0* | 91.2* | 94.0 | 94.4 | 92.4 | 95.4 |
| HMMT 2025 | Avg@32 | 38.8 | 27.5 | 11.9 | 15.9 | 15.9 | 19.4 | 34.7 |
| CNMO 2024 | Avg@16 | 74.3 | 74.7 | 48.6 | 60.4 | 57.6 | 56.6 | 75.0 |
| PolyMath-en | Avg@4 | 65.1 | 59.5 | 51.9 | 52.8 | 49.8 | 54.0 | 49.9 |
| ZebraLogic | Acc | 89.0 | 84.0 | 37.7* | 73.7 | 59.3 | 58.5 | 57.9 |
| AutoLogi | Acc | 89.5 | 88.9 | 83.3 | 89.8 | 86.1 | 88.2 | 84.1 |
| GPQA-Diamond | Avg@8 | 75.1 | 68.4* | 62.9* | 70.0* | 74.9* | 66.3 | 68.2 |
| SuperGPQA | Acc | 57.2 | 53.7 | 50.2 | 55.7 | 56.5 | 50.8 | 49.6 |
| Humanity’s Last Exam (Text Only) |
– | 4.7 | 5.2 | 5.7 | 5.8 | 7.1 | 3.7 | 5.6 |
| General Tasks | ||||||||
| MMLU | EM | 89.5 | 89.4 | 87.0 | 91.5 | 92.9 | 90.4 | 90.1 |
| MMLU-Redux | EM | 92.7 | 90.5 | 89.2 | 93.6 | 94.2 | 92.4 | 90.6 |
| MMLU-Pro | EM | 81.1 | 81.2* | 77.3 | 83.7 | 86.6 | 81.8 | 79.4 |
| IFEval | Prompt Strict | 89.8 | 81.1 | 83.2* | 87.6 | 87.4 | 88.0 | 84.3 |
| Multi-Challenge | Acc | 54.1 | 31.4 | 34.0 | 46.8 | 49.0 | 36.4 | 39.5 |
| SimpleQA | Correct | 31.0 | 27.7 | 13.2 | 15.9 | 22.8 | 42.3 | 23.3 |
| Livebench | Pass@1 | 76.4 | 72.4 | 67.6 | 74.8 | 74.6 | 69.8 | 67.8 |
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Deepseek – ChatSonic
Meta: Llama 4 Scout
Meta: Llama 4 Scout
Llama 4 Scout 17B Instruct (16E) is a Mixture-of-Experts (MoE) model from Meta, activating 17B parameters out of 109B total. It supports native multimodal input (text + images) and generates multilingual text and code across 12 languages. With 16 experts per forward pass, Scout is optimized for assistant-style interaction, visual reasoning, and large-scale context handling—supporting up to 10M tokens and trained on a ~40T-token corpus.
Engineered for efficiency and flexible deployment, Scout uses early fusion for smooth multimodal integration and is instruction-tuned for tasks like multilingual chat, captioning, and image understanding. Released under the Llama 4 Community License, it was trained on data up to August 2024 and made publicly available on April 5, 2025.
| Creator | Meta |
| Release Date | April, 2025 |
| License | Llama 4 Comunity Lisense Agreement |
| Context Window | 128,000 |
| Image Input Support | Yes |
| Open Source (Weights) | Yes |
| Parameters | 109B, 17B active at inference time |
| Model Weights | Click here |
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DeepSeek: DeepSeek R1 0528
DeepSeek: DeepSeek R1 0528
Update (May 28): The original DeepSeek R1 now delivers performance comparable to OpenAI’s o1, but is fully open-source with transparent reasoning tokens. The model has 671B parameters, with 37B active per inference pass—making it one of the largest openly available models.
| Creator | Deepseek |
| Release Date | May, 2025 |
| License | MIT |
| Context Window | 128,000 |
| Image Input Support | No |
| Open Source (Weights) | Yes |
| Parameters | 685B, 37B active at inference time |
| Model Weights | Click here |
