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. | — | 80B |
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. | 1M | 256k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $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. | $2.50 | — |
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. | $0.03 | — |
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. | $0.15Google | $0.12Parasail |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.25Google | $0.80Parasail |
| $185Google list price | $64.00Parasail · bf16 | |
| Benchmarks | ||
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. | 54.2% | 44.3% |
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. | 66% | — |
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. | — | 70.6% |
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 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | 54% | — |
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% |
BU BenchBrowser agent — Can the AI drive a real web browser to finish tasks — clicking, filling forms, and navigating sites the way a person would? Run by Browser Use on their BU Bench task set. Higher is better. | 49% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | 74% | — |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | 1140 | — |
| Overview | ||
| Company | Qwen | |
| Release date | Jul 21 2026 | Feb 3 2026 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Gemini 3.5 Flash-LitevsClaude Haiku 5.5Qwen3-Coder-NextvsClaude Haiku 5.5Gemini 3.5 Flash-LitevsGPT-6.1 TerraQwen3-Coder-NextvsGPT-6.1 TerraGemini 3.5 Flash-LitevsMuse Spark OpenQwen3-Coder-NextvsMuse Spark OpenGemini 3.5 Flash-LitevsGrok 4.7Qwen3-Coder-NextvsGrok 4.7Gemini 3.5 Flash-LitevsDeepSeek-V4.1-Flash-1017Qwen3-Coder-NextvsDeepSeek-V4.1-Flash-1017Gemini 3.5 Flash-LitevsMistral Large 4Qwen3-Coder-NextvsMistral Large 4Frequently asked questions
Gemini 3.5 Flash-Lite leads Qwen3-Coder-Next on 1 of the 1 benchmark they both report (SWE-Bench Pro). Only Gemini 3.5 Flash-Lite has a verified first-party price for an available API: $0.30 per million input tokens and $2.50 per million output tokens. No currently available pay-as-you-go API rate is tracked for Qwen3-Coder-Next. Qwen3-Coder-Next shipped 168 days before Gemini 3.5 Flash-Lite, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 3.5 Flash-Lite) vs 256k (Qwen3-Coder-Next). Gemini 3.5 Flash-Lite is closed, while Qwen3-Coder-Next is open weight.
On SWE-Bench Pro, Gemini 3.5 Flash-Lite leads at 54.2% vs Qwen3-Coder-Next at 44.3%.