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. | 671B | 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. | 128k | 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. | — | $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. | — | $1.50 |
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. | $0.27SiliconFlow | $0.12Parasail |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.00SiliconFlow | $0.80Parasail |
| $104SiliconFlow · fp8 | $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. | 68.4% | 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. | 57.8% | 62.8% |
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% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | 74.9% | — |
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% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 80.7% | — |
| Overview | ||
| Company | DeepSeek | Qwen |
| Release date | Sep 22 2025 | Feb 3 2026 |
| Access | Open Weight | Open Weight |
| Model details | View model | View model |
Other comparisons
DeepSeek V3.1 TerminusvsClaude Haiku 5.5Qwen3-Coder-NextvsClaude Haiku 5.5DeepSeek V3.1 TerminusvsGPT-6.1 SolQwen3-Coder-NextvsGPT-6.1 SolDeepSeek V3.1 TerminusvsGemini 4 ArgonQwen3-Coder-NextvsGemini 4 ArgonDeepSeek V3.1 TerminusvsMuse Spark OpenQwen3-Coder-NextvsMuse Spark OpenDeepSeek V3.1 TerminusvsGrok 4.7Qwen3-Coder-NextvsGrok 4.7DeepSeek V3.1 TerminusvsMistral Large 4Qwen3-Coder-NextvsMistral Large 4Frequently asked questions
Qwen3-Coder-Next leads DeepSeek V3.1 Terminus on 2 of the 2 benchmarks they both report (SWE-Bench Verified, SWE-Bench Multilingual). Only Qwen3-Coder-Next has a verified first-party API price: $0.30 per million input tokens and $1.50 per million output tokens. No pay-as-you-go API rate is tracked for DeepSeek V3.1 Terminus. DeepSeek V3.1 Terminus shipped 134 days before Qwen3-Coder-Next, so benchmark comparisons should account for the intervening progress.
DeepSeek V3.1 Terminus has 671B parameters, while Qwen3-Coder-Next has 80B. Context windows are 128k (DeepSeek V3.1 Terminus) vs 256k (Qwen3-Coder-Next).
On SWE-Bench Verified, Qwen3-Coder-Next leads at 70.6% vs DeepSeek V3.1 Terminus at 68.4%. On SWE-Bench Multilingual, Qwen3-Coder-Next leads at 62.8% vs DeepSeek V3.1 Terminus at 57.8%.