Model comparison
Claude Sonnet 4 vs DeepSeek-V3.1
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 40.8 on the Noometry Index.
Last verified . 27 shared benchmarks.
DeepSeekSummary
- They share 27 benchmarks with published results for both. Claude Sonnet 4 scores higher in 2 categories and DeepSeek-V3.1 in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek-V3.1 leads 27.9 to 22.9.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 73% for Claude Sonnet 4 and 53.2% for DeepSeek-V3.1.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $3 / $15 for Claude Sonnet 4.
- Claude Sonnet 4 accepts more context: 200K tokens versus 164K.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.
Side by side
| Claude Sonnet 4 | DeepSeek-V3.1 | |
|---|---|---|
| Provider | Anthropic | DeepSeek |
| Noometry Index | 40.8 | 42.8 |
| Released | 2025-05-22 | 2025-08-21 |
| Weights | Proprietary | Open |
| Context window | 200K | 164K |
| Max output | 64K | 8K |
| Input $ / M tokens | $3 | $0.25 |
| Output $ / M tokens | $15 | $0.95 |
| Results tracked | 58 | 27 |
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Category by category
Coding Claude Sonnet 4 leads
Claude Sonnet 4: 43.5 (#88), DeepSeek-V3.1: 40.3 (#144)
| Benchmark | Claude Sonnet 4 | DeepSeek-V3.1 |
|---|---|---|
| WeirdML | 46.1% | 38.4% |
| LMArena Coding | 1414 | 1417 |
| SWE-bench Verified (bash only) | 64.9% | — |
| Aider Polyglot | 61.3% | — |
| SciCode | 40% | — |
| GSO | 4.9% | — |
| ALE-Bench | 655.35 | — |
Agentic & Tool Use Not comparable
Claude Sonnet 4: 38.5 (#31), DeepSeek-V3.1: —
| Benchmark | Claude Sonnet 4 | DeepSeek-V3.1 |
|---|---|---|
| TheAgentCompany | 33.1% | — |
| Cybench | 35% | — |
| DeepResearch Bench | 46.6% | — |
| OSWorld | 43.9% | — |
| METR Time Horizons | 62% | — |
Reasoning DeepSeek-V3.1 leads
Claude Sonnet 4: 22.9 (#187), DeepSeek-V3.1: 27.9 (#110)
| Benchmark | Claude Sonnet 4 | DeepSeek-V3.1 |
|---|---|---|
| SimpleBench | 45.5% | 40% |
| Kagi LLM Benchmark | 73% | 53.2% |
| LMArena Hard Prompts | 1372 | 1417 |
| DTBench | 77.1% | 82.7% |
| LMCA | 29% | 24.3% |
| Epoch Capabilities Index | 141.69 | 139.92 |
| ForecastBench | 60.2 | 58 |
| ARC-AGI-2 | 5.9% | — |
| ARC-AGI-1 | 40% | — |
| CritPt | 0.3% | — |
| EnigmaEval | 3.1% | — |
Math Claude Sonnet 4 leads
Claude Sonnet 4: 43.3 (#80), DeepSeek-V3.1: 38.9 (#122)
| Benchmark | Claude Sonnet 4 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Math | 1375 | 1420 |
| OTIS Mock AIME 2024-2025 | 71.1% | — |
| Omni-MATH | 60.2% | — |
| MATH Level 5 | 84.4% | — |
| FrontierMath (Feb 2025 set) | 4.1% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge DeepSeek-V3.1 leads
Claude Sonnet 4: 41.8 (#108), DeepSeek-V3.1: 43.7 (#90)
| Benchmark | Claude Sonnet 4 | DeepSeek-V3.1 |
|---|---|---|
| Vectara Hallucination Rate | 10.3% | 5.5% |
| LMArena Expert | 1372 | 1405 |
| GPQA Diamond | 79.2% | — |
| Humanity's Last Exam | 7.8% | — |
| MMLU-Pro | 84.3% | — |
| Confabulations | 13.2% | — |
| GPQA (HELM) | 70.6% | — |
Multimodal Not comparable
Claude Sonnet 4: 26.2 (#121), DeepSeek-V3.1: —
| Benchmark | Claude Sonnet 4 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Vision | 1191 | — |
| GeoBench | 37% | — |
| VPCT | 34% | — |
| MindCube | 44.8% | — |
Multilingual DeepSeek-V3.1 leads
Claude Sonnet 4: 46.7 (#156), DeepSeek-V3.1: 51.6 (#106)
| Benchmark | Claude Sonnet 4 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Non-English | 1333 | 1400 |
| LMArena Chinese | 1350 | 1469 |
| LMArena French | 1363 | 1447 |
| LMArena German | 1331 | 1411 |
| LMArena Japanese | 1302 | 1378 |
| LMArena Korean | 1291 | 1337 |
| LMArena Russian | 1355 | 1405 |
| LMArena Spanish | 1357 | 1431 |
Instruction Following DeepSeek-V3.1 leads
Claude Sonnet 4: 71.7 (#145), DeepSeek-V3.1: 73.9 (#110)
| Benchmark | Claude Sonnet 4 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Instruction Following | 1376 | 1400 |
| IFEval | 84% | — |
Long Context DeepSeek-V3.1 leads
Claude Sonnet 4: 33.7 (#259), DeepSeek-V3.1: 36.3 (#232)
| Benchmark | Claude Sonnet 4 | DeepSeek-V3.1 |
|---|---|---|
| Fiction.LiveBench | 46.9% | 52.8% |
| LMArena Longer Query | 1398 | 1422 |
Writing & Preference DeepSeek-V3.1 leads
Claude Sonnet 4: 57.1 (#132), DeepSeek-V3.1: 60.3 (#98)
| Benchmark | Claude Sonnet 4 | DeepSeek-V3.1 |
|---|---|---|
| LMArena Text | 1351 | 1420 |
| LMArena Creative Writing | 1345 | 1401 |
| EQ-Bench Creative Writing | 1483 | 1436 |
| LMArena Multi-Turn | 1376 | 1408 |
| Short-Story Creative Writing | 81.4% | — |
| WildBench | 83.8% | — |
Frequently asked questions
Is Claude Sonnet 4 better than DeepSeek-V3.1?
DeepSeek-V3.1 is the stronger model overall, scoring 42.8 to 40.8 on the Noometry Index.
Which is cheaper, Claude Sonnet 4 or DeepSeek-V3.1?
DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Claude Sonnet 4 lists at $3 and $15.
Is Claude Sonnet 4 or DeepSeek-V3.1 better for coding?
Claude Sonnet 4 scores higher on coding benchmarks: 43.5 versus 40.3 in the Noometry coding category.
Which has the bigger context window?
Claude Sonnet 4 does, with 200K tokens against 164K.
How many benchmarks do Claude Sonnet 4 and DeepSeek-V3.1 share?
27 benchmarks have published results for both models. Claude Sonnet 4 has 58 scored results on Noometry and DeepSeek-V3.1 has 27.