Model comparison
DeepSeek-V3.1-Terminus vs GPT-5 Nano
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 33.5 on the Noometry Index. GPT-5 Nano costs 3.3× less per token, which makes it the better buy when DeepSeek-V3.1-Terminus's lead doesn't matter for your workload.
Last verified . 14 shared benchmarks.
DeepSeekSummary
- They share 14 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 6 categories and GPT-5 Nano in 1 category; 6 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where DeepSeek-V3.1-Terminus leads 61.0 to 39.1.
- The biggest single-benchmark swing is LMCA: 28.6% for DeepSeek-V3.1-Terminus and 7.9% for GPT-5 Nano.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.27 / $1 for DeepSeek-V3.1-Terminus.
- GPT-5 Nano accepts more context: 400K tokens versus 164K.
- DeepSeek-V3.1-Terminus has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1-Terminus | GPT-5 Nano | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 43.1 | 33.5 |
| Released | 2025-09-22 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 164K | 400K |
| Max output | 147K | 128K |
| Input $ / M tokens | $0.27 | $0.05 |
| Output $ / M tokens | $1 | $0.40 |
| Results tracked | 16 | 49 |
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Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 42.0 (#113), GPT-5 Nano: 33.6 (#254)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Nano |
|---|---|---|
| LMArena Coding | 1426 | 1351 |
| ALE-Bench | 745.17 | 718.67 |
| SWE-bench Verified (bash only) | — | 34.8% |
| SciCode | 40.6% | — |
| WeirdML | — | 38.1% |
Agentic & Tool Use Not comparable
DeepSeek-V3.1-Terminus: —, GPT-5 Nano: 25.8 (#106)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Nano |
|---|---|---|
| Terminal-Bench | — | 21.8% |
| Berkeley Function Calling Leaderboard | — | 51.5% |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 26.4 (#133), GPT-5 Nano: 16.3 (#306)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Nano |
|---|---|---|
| Kagi LLM Benchmark | 57.4% | 62.2% |
| LMArena Hard Prompts | 1426 | 1328 |
| DTBench | 81.3% | 62.7% |
| LMCA | 28.6% | 7.9% |
| ARC-AGI-2 | — | 2.6% |
| ARC-AGI-1 | — | 20.7% |
| CritPt | 1.7% | — |
| Chess Puzzles | — | 27% |
| Mystery Game Puzzles | — | 9% |
| Epoch Capabilities Index | — | 139.38 |
| ForecastBench | — | 59.1 |
Math DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 38.5 (#137), GPT-5 Nano: 29.4 (#241)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Nano |
|---|---|---|
| LMArena Math | 1402 | 1317 |
| FrontierMath (Tiers 1-3) | — | 20% |
| FrontierMath Tier 4 | — | 2.4% |
| OTIS Mock AIME 2024-2025 | — | 81.1% |
| ProofBench | — | 12% |
| Omni-MATH | — | 54.6% |
| MATH Level 5 | — | 95.2% |
| FrontierMath (Feb 2025 set) | — | 8.3% |
| FrontierMath Tier 4 (v1) | — | 2.1% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, GPT-5 Nano: 35.9 (#178)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Nano |
|---|---|---|
| GPQA Diamond | — | 69.4% |
| SimpleQA Verified | — | 11.7% |
| MMLU-Pro | — | 77.8% |
| Vectara Hallucination Rate | — | 10.5% |
| GPQA (HELM) | — | 67.9% |
| LMArena Expert | — | 1321 |
Multimodal Not comparable
DeepSeek-V3.1-Terminus: —, GPT-5 Nano: 31.3 (#108)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Nano |
|---|---|---|
| LMArena Vision | — | 1159 |
| VPCT | — | 37.2% |
Multilingual DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 52.1 (#92), GPT-5 Nano: 45.3 (#172)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Nano |
|---|---|---|
| LMArena Non-English | 1407 | 1313 |
| LMArena Russian | 1436 | 1296 |
| LMArena Chinese | — | 1356 |
| LMArena German | — | 1327 |
| LMArena Japanese | — | 1226 |
| LMArena Korean | — | 1269 |
| LMArena Spanish | — | 1360 |
Instruction Following Too close to call
DeepSeek-V3.1-Terminus: 74.0 (#106), GPT-5 Nano: 75.0 (#79)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Nano |
|---|---|---|
| LMArena Instruction Following | 1404 | 1306 |
| IFEval | — | 93.2% |
Long Context DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 43.4 (#97), GPT-5 Nano: 31.3 (#281)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Nano |
|---|---|---|
| LMArena Longer Query | 1421 | 1312 |
| Fiction.LiveBench | — | 44.4% |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.0 (#92), GPT-5 Nano: 39.1 (#249)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Nano |
|---|---|---|
| LMArena Text | 1419 | 1320 |
| LMArena Creative Writing | 1403 | 1249 |
| LMArena Multi-Turn | 1411 | 1311 |
| EQ-Bench Creative Writing | — | 705 |
| WildBench | — | 80.6% |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than GPT-5 Nano?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 33.5 on the Noometry Index. GPT-5 Nano costs 3.3× 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-5 Nano?
GPT-5 Nano is cheaper. It lists at $0.05 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-5 Nano better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 33.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Nano does, with 400K tokens against 164K.
How many benchmarks do DeepSeek-V3.1-Terminus and GPT-5 Nano share?
14 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and GPT-5 Nano has 49.