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. | 8B/70B/405B | 480B |
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. | 128k | 256k |
| 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. | $0.02DeepInfra | $0.30DeepInfra |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.04DeepInfra | $1.00DeepInfra |
| $6.00DeepInfra · fp8 | $110DeepInfra · turbo · fp4 | |
| Benchmarks | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 14% | 20% |
| Overview | ||
| Company | Meta | Qwen |
| Release date | Jul 23 2024 | Jul 22 2025 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
LLaMA 3.1vsClaude Haiku 5.5Qwen3-CodervsClaude Haiku 5.5LLaMA 3.1vsGPT-6.1 TerraQwen3-CodervsGPT-6.1 TerraLLaMA 3.1vsGemini 4 CarbonQwen3-CodervsGemini 4 CarbonLLaMA 3.1vsGrok 4.7Qwen3-CodervsGrok 4.7LLaMA 3.1vsDeepSeek-V4.1-Flash-1017Qwen3-CodervsDeepSeek-V4.1-Flash-1017LLaMA 3.1vsMistral Large 4Qwen3-CodervsMistral Large 4Frequently asked questions
Qwen3-Coder leads LLaMA 3.1 on 1 of the 1 benchmark they both report (BullshitBench v2). LLaMA 3.1 shipped 364 days before Qwen3-Coder, so benchmark comparisons should account for the intervening progress.
LLaMA 3.1 has 8B/70B/405B parameters, while Qwen3-Coder has 480B. Context windows are 128k (LLaMA 3.1) vs 256k (Qwen3-Coder).
On BullshitBench v2, Qwen3-Coder leads at 20% vs LLaMA 3.1 at 14%.