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. | — | 235B |
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. | — | 128k |
| 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. | $3.00 | $0.70 |
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. | $15.00 | $2.80 |
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. | $0.30 | — |
Cheapest inputLowest input rate across third-party providers, excluding the lab itself. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $3.00Amazon Bedrock | $0.0482StreamLake |
Cheapest outputLowest output rate across third-party providers, excluding the lab itself. May come from a different provider than the cheapest input. | $15.00Amazon Bedrock | $0.1931StreamLake |
| 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. | 79% | 6% |
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. | 77.2% | — |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | 39% | — |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | 13.6% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 83.4% | — |
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. | 61.4% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 68% | — |
| Overview | ||
| Company | Anthropic | Qwen |
| Release date | Sep 29 2025 | Apr 29 2025 |
| Access | Closed | Open Weight |
| Model details | View model | View model |
Other comparisons
Claude Sonnet 4.5vsGPT-6.1 SolQwen3vsGPT-6.1 SolClaude Sonnet 4.5vsGemini 4 ArgonQwen3vsGemini 4 ArgonClaude Sonnet 4.5vsMuse Spark 1.3Qwen3vsMuse Spark 1.3Claude Sonnet 4.5vsGrok 4.7Qwen3vsGrok 4.7Claude Sonnet 4.5vsDeepSeek-V4.1-FlashQwen3vsDeepSeek-V4.1-FlashClaude Sonnet 4.5vsMistral Large 4Qwen3vsMistral Large 4Frequently asked questions
Claude Sonnet 4.5 leads Qwen3 on 1 of the 1 benchmark they both report (BullshitBench v2). Qwen3 is cheaper on both input and output: $0.70 vs $3.00 per million input tokens, and $2.80 vs $15.00 per million output tokens. Figures are base-tier rates. Qwen3 shipped 153 days before Claude Sonnet 4.5, so benchmark comparisons should account for the intervening progress.
Claude Sonnet 4.5 is closed, while Qwen3 is open weight.
On BullshitBench v2, Claude Sonnet 4.5 leads at 79% vs Qwen3 at 6%.