Compare AI models
| Specifications | ||
ParametersThe learned values that shape how a model responds. B means billion and T means trillion. For mixture-of-experts models, the total count is larger than the number active for each token. Parameter count alone does not measure quality. | 70B | 2.4T |
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 16k | 1M |
| API pricingUSD per 1M tokens · lower wins | ||
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | — | $2.00Alibaba |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $6.00Alibaba |
| — | $700Alibaba | |
| These models have no shared benchmark scores. | ||
DeepSWE 1.1Agentic coding — Artificial Analysis' independent test of deep, agentic software-engineering work — the AI has to plan and carry out substantial coding tasks end to end. (Version 1.1 of the test.) Higher is better. | — | 69.3% |
NL2Repo-BenchRepo-level code generation — Tests whether the AI can turn a natural-language requirement into working code across an entire repository, not just produce a single function or patch. Higher is better. | — | 64.9% |
QwenSWEBench V2Software engineering — Qwen's in-house coding benchmark, second version, built around complex real-world software-engineering tasks. Scores are not comparable with the first version. Higher is better. | — | 70% |
Terminal-Bench 4.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Version 4.0 recalibrated how much time, CPU and memory each task gets, removed eight tasks and fixed nineteen, so fewer runs fail for reasons that have nothing to do with the model. Scores are not comparable with earlier versions. Higher is better. | — | 26.97% |
Terminal-Bench 3.0Agentic terminal coding — command-line task completion (v3.0, much harder task set) | — | 29% |
JobBenchProfessional tool use — Tests the AI on professional workplace tasks that require using real work tools — the kind of multi-step jobs an office worker handles. Higher is better. | — | 64% |
CoWorkBenchLong-horizon office work — Tests long-running office tasks across fields including computer science, finance, law, medicine, and other productivity work. Higher is better. | — | 76.1% |
Toolathlon-VerifiedPersonal tool use — Tests how well the AI uses everyday personal tools and apps to get things done — a human-checked version of Toolathlon. Higher is better. | — | 73.3% |
AutomationBenchBusiness workflows — Tests whether the AI can run real multi-step business workflows — the kind of end-to-end office processes companies want to automate — from start to finish. Higher is better. | — | 50.8% |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | — | 82.7% |
| Overview | ||
| Company | Meta | Qwen |
| Release date | Jan 29 2024 | Sep 2 2026 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Code Llama 70BvsClaude Haiku 5.5Qwen3.8-Max-0902vsClaude Haiku 5.5Code Llama 70BvsGPT-6.1 SolQwen3.8-Max-0902vsGPT-6.1 SolCode Llama 70BvsGemini 4 ArgonQwen3.8-Max-0902vsGemini 4 ArgonCode Llama 70BvsGrok 4.7Qwen3.8-Max-0902vsGrok 4.7Code Llama 70BvsDeepSeek-V4.1-Flash-1017Qwen3.8-Max-0902vsDeepSeek-V4.1-Flash-1017Code Llama 70BvsMistral Large 4Qwen3.8-Max-0902vsMistral Large 4Frequently asked questions
Code Llama 70B and Qwen3.8-Max-0902 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Code Llama 70B shipped 947 days before Qwen3.8-Max-0902, so benchmark comparisons should account for the intervening progress.
Code Llama 70B has 70B parameters, while Qwen3.8-Max-0902 has 2.4T. Context windows are 16k (Code Llama 70B) vs 1M (Qwen3.8-Max-0902). Code Llama 70B is open weight, while Qwen3.8-Max-0902 is closed.
Direct benchmark comparisons are unavailable — Code Llama 70B and Qwen3.8-Max-0902 don't publish scores on any of the same benchmarks.