Model comparison
DeepSeek-V3.1-Terminus vs GPT-4.1 nano
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 27.9 on the Noometry Index. GPT-4.1 nano costs 2.6× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
DeepSeekSummary
- They share 15 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 7 categories and GPT-4.1 nano in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.0 to 40.5.
- The biggest single-benchmark swing is DTBench: 81.3% for DeepSeek-V3.1-Terminus and 52.5% for GPT-4.1 nano.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.27 / $1 for DeepSeek-V3.1-Terminus.
- GPT-4.1 nano accepts more context: 1.05M tokens versus 164K.
- DeepSeek-V3.1-Terminus has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1-Terminus | GPT-4.1 nano | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 43.1 | 27.9 |
| Released | 2025-09-22 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 147K | 33K |
| Input $ / M tokens | $0.27 | $0.10 |
| Output $ / M tokens | $1 | $0.40 |
| Results tracked | 16 | 38 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 42.0 (#113), GPT-4.1 nano: 24.1 (#330)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-4.1 nano |
|---|---|---|
| SciCode | 40.6% | 25.9% |
| LMArena Coding | 1426 | 1306 |
| Aider Polyglot | — | 8.9% |
| WeirdML | — | 19% |
| ALE-Bench | 745.17 | — |
Agentic & Tool Use Not comparable
DeepSeek-V3.1-Terminus: —, GPT-4.1 nano: 26.5 (#104)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-4.1 nano |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 33% |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 26.4 (#133), GPT-4.1 nano: 8.5 (#349)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-4.1 nano |
|---|---|---|
| Kagi LLM Benchmark | 57.4% | 33.3% |
| CritPt | 1.7% | 0% |
| LMArena Hard Prompts | 1426 | 1286 |
| DTBench | 81.3% | 52.5% |
| LMCA | 28.6% | 5.5% |
| ARC-AGI-2 | — | 0% |
| ARC-AGI-1 | — | 0% |
| Epoch Capabilities Index | — | 129.62 |
Math DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 38.5 (#137), GPT-4.1 nano: 26.9 (#252)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-4.1 nano |
|---|---|---|
| LMArena Math | 1402 | 1274 |
| OTIS Mock AIME 2024-2025 | — | 28.9% |
| Omni-MATH | — | 36.7% |
| MATH Level 5 | — | 70% |
| FrontierMath (Feb 2025 set) | — | 1% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, GPT-4.1 nano: 21.8 (#273)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-4.1 nano |
|---|---|---|
| GPQA Diamond | — | 48.9% |
| SimpleQA Verified | — | 6% |
| MMLU-Pro | — | 55% |
| GPQA (HELM) | — | 50.7% |
| LMArena Expert | — | 1272 |
Multimodal Not comparable
DeepSeek-V3.1-Terminus: —, GPT-4.1 nano: 29.2 (#113)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-4.1 nano |
|---|---|---|
| LMArena Vision | — | 1063 |
Multilingual DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 52.1 (#92), GPT-4.1 nano: 41.6 (#205)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-4.1 nano |
|---|---|---|
| LMArena Non-English | 1407 | 1260 |
| LMArena Russian | 1436 | 1261 |
| LMArena Chinese | — | 1270 |
| LMArena German | — | 1288 |
| LMArena Japanese | — | 1198 |
Instruction Following DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 74.0 (#106), GPT-4.1 nano: 67.8 (#193)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-4.1 nano |
|---|---|---|
| LMArena Instruction Following | 1404 | 1267 |
| IFEval | — | 84.3% |
Long Context DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 43.4 (#97), GPT-4.1 nano: 23.7 (#296)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-4.1 nano |
|---|---|---|
| LMArena Longer Query | 1421 | 1283 |
| Fiction.LiveBench | — | 25% |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.0 (#92), GPT-4.1 nano: 40.5 (#243)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-4.1 nano |
|---|---|---|
| LMArena Text | 1419 | 1285 |
| LMArena Creative Writing | 1403 | 1260 |
| LMArena Multi-Turn | 1411 | 1277 |
| EQ-Bench Creative Writing | — | 946 |
| WildBench | — | 81.2% |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than GPT-4.1 nano?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 27.9 on the Noometry Index. GPT-4.1 nano costs 2.6× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1-Terminus or GPT-4.1 nano?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; DeepSeek-V3.1-Terminus lists at $0.27 and $1.
Is DeepSeek-V3.1-Terminus or GPT-4.1 nano better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 24.1 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3.1-Terminus and GPT-4.1 nano share?
15 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and GPT-4.1 nano has 38.