Developer AI
Compare models for code generation, repository reasoning, terminal work and tool-driven agents. Long context helps, but reliable tool use, edit quality and runtime integration usually matter more than a headline benchmark.
Selection approach
Select against the repositories, languages, test suites and agent framework you will actually use. A model that fits memory can still be a poor choice if tool calls, patch quality or long-running task recovery are weak.
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12 current models
Showing 12 models
Moonshot AI
A 2.8-trillion-parameter, 104-billion-active multimodal mixture-of-experts model for long-context reasoning, coding, visual understanding and agentic work.
Qwen
A 36-billion-parameter multimodal mixture-of-experts model with about 3 billion active parameters, a 262,144-token default context and a strong emphasis on agentic coding.
DeepSeek
A 284B-total, 13B-active mixture-of-experts language model with one-million-token context and a mixed FP4/FP8 release format.
Mistral AI
A 119B-total, 6B-active hybrid model that combines instruction following, reasoning, coding-agent and multimodal capabilities.
OpenAI
OpenAI's 117B-total, 5.1B-active open-weight reasoning and agentic model, released with native MXFP4 MoE weights.
OpenAI
A 21-billion-parameter sparse reasoning model with 3.6 billion active parameters, native tool use and an MXFP4 release intended for local or specialised work.
Qwen
An 80-billion-total, 3-billion-active sparse model designed for coding agents, local development and long repository context.
Qwen
A compact 4-billion-parameter multimodal model with native vision, reasoning, coding and broad multilingual support.
Qwen
A 27-billion-parameter dense multimodal model for reasoning, coding, agents and visual understanding across 201 languages and dialects.
StepFun
A 198-billion-parameter sparse vision-language model with about 11 billion active parameters, designed for reasoning, coding, agents and visual intelligence.
Z.ai
A 744-billion-total, 40-billion-active sparse model aimed at complex systems engineering and long-horizon agentic tasks.
MiniMax
A sparse agentic model trained for coding, tool use, search and office work across more than ten programming languages.
No model matches that search and workload combination.
Model-to-hardware fit
Use the precise model version, numerical format and complete software pipeline intended for production.
Check whether it can load on one GPU or requires supported multi-GPU loading.
Allow for context, cache, batch, media encoders and concurrent users.
Measure quality, latency and stability on representative work.
Model weights are only one part of the system. Data access, runtime software, storage, monitoring and administrator handover also affect a reliable deployment.
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