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. | 397B | — |
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 | 1M |
| 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. | $0.60 | — |
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. | $3.60 | — |
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. | $0.065Alibaba | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.13Darkbloom | — |
| $22.50Darkbloom · fp4 | — | |
| These models have no shared benchmark scores. | ||
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. | 78% | — |
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. | 76.4% | — |
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. | 69.3% | — |
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. | 52.5% | — |
BrowseCompWeb browsing — Can the AI browse the web and track down hard-to-find answers? Higher is better. | 69% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 28.7% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 88.4% | — |
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. | 62.2% | — |
CharXiv ReasoningChart reasoning — Can the AI read and reason about complex charts and figures, not just text? Higher is better. | 80.8% | — |
MMMU-ProMultimodal reasoning — A tougher version of MMMU — college-level questions that mix images, diagrams, and text together. Higher is better. | 79% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 85% | — |
| Overview | ||
| Company | Qwen | SpaceXSI |
| Release date | Feb 16 2026 | Feb 17 2025 |
| Access | Open Weight | Closed |
| Model details | View model | View model |
Other comparisons
Qwen3.5vsClaude Haiku 5.5Grok 3vsClaude Haiku 5.5Qwen3.5vsGPT-6.1 SolGrok 3vsGPT-6.1 SolQwen3.5vsGemini 4 ArgonGrok 3vsGemini 4 ArgonQwen3.5vsMuse Spark OpenGrok 3vsMuse Spark OpenQwen3.5vsDeepSeek-V4.1-Flash-1017Grok 3vsDeepSeek-V4.1-Flash-1017Qwen3.5vsMistral Large 4Grok 3vsMistral Large 4Frequently asked questions
Qwen3.5 and Grok 3 don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Only Qwen3.5 has a verified first-party API price: $0.60 per million input tokens and $3.60 per million output tokens. No pay-as-you-go API rate is tracked for Grok 3. Grok 3 shipped 364 days before Qwen3.5, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Qwen3.5) vs 1M (Grok 3). Qwen3.5 is open weight, while Grok 3 is closed.
Direct benchmark comparisons are unavailable — Qwen3.5 and Grok 3 don't publish scores on any of the same benchmarks.