Compare AI models
| Specifications | ||
ParametersA rough measure of how big the model is. More parameters usually means more capable and more expensive to run, though it is a poor guide on its own — a smaller, newer model often beats a larger, older one. | — | 80B |
Context windowHow much text the model can hold in mind at once — your question, any documents you attach, the conversation so far, and its own reply. Go past it and the earliest part falls out of view. | — | 256k |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $2.00 | $0.30 |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $8.00 | $1.50 |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.50 | — |
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. | $2.00Azure | $0.12Parasail |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $8.00Azure | $0.80Parasail |
| $800OpenAI list price | $64.00Parasail · bf16 | |
| Benchmarks | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 54.6% | 70.6% |
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% | — |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 44.3% |
SWE-Bench MultilingualMultilingual coding — Like SWE-Bench, but the coding problems span many programming languages, not just one. Tests how broadly the AI can code. Higher is better. | — | 62.8% |
Terminal-Bench 2.0Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. | — | 36.2% |
| Overview | ||
| Company | OpenAI | Qwen |
| Release date | Apr 14 2025 | Feb 3 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
GPT-4.1vsClaude Haiku 5.5Qwen3-Coder-NextvsClaude Haiku 5.5GPT-4.1vsGemini 4 ArgonQwen3-Coder-NextvsGemini 4 ArgonGPT-4.1vsMuse WonderQwen3-Coder-NextvsMuse WonderGPT-4.1vsGrok 4.7Qwen3-Coder-NextvsGrok 4.7GPT-4.1vsDeepSeek-V4.1-FlashQwen3-Coder-NextvsDeepSeek-V4.1-FlashGPT-4.1vsMistral Large 4Qwen3-Coder-NextvsMistral Large 4Frequently asked questions
Qwen3-Coder-Next leads GPT-4.1 on 1 of the 1 benchmark they both report (SWE-Bench Verified). Qwen3-Coder-Next is cheaper on both input and output: $0.30 vs $2.00 per million input tokens, and $1.50 vs $8.00 per million output tokens. Figures are base-tier rates. GPT-4.1 shipped 295 days before Qwen3-Coder-Next, so benchmark comparisons should account for the intervening progress.
GPT-4.1 is closed, while Qwen3-Coder-Next is open weight.
On SWE-Bench Verified, Qwen3-Coder-Next leads at 70.6% vs GPT-4.1 at 54.6%.