Model comparison
DeepSeek-V3.1-Terminus vs GPT-5 Mini
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 41.8 on the Noometry Index.
Last verified . 16 shared benchmarks.
DeepSeekSummary
- They share 16 benchmarks with published results for both. DeepSeek-V3.1-Terminus scores higher in 5 categories and GPT-5 Mini in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5 Mini leads 46.7 to 38.5.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 57.4% for DeepSeek-V3.1-Terminus and 70.3% for GPT-5 Mini.
- DeepSeek-V3.1-Terminus is cheaper at $0.27 / $1 per million input/output tokens, against $0.25 / $2 for GPT-5 Mini.
- GPT-5 Mini 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 Mini | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 43.1 | 41.8 |
| Released | 2025-09-22 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 164K | 400K |
| Max output | 147K | 128K |
| Input $ / M tokens | $0.27 | $0.25 |
| Output $ / M tokens | $1 | $2 |
| Results tracked | 16 | 60 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 42.0 (#113), GPT-5 Mini: 40.1 (#146)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Mini |
|---|---|---|
| SciCode | 40.6% | 39.2% |
| LMArena Coding | 1426 | 1406 |
| ALE-Bench | 745.17 | 799.77 |
| SWE-bench Verified | — | 64.7% |
| SWE-bench Verified (bash only) | — | 59.8% |
| SWE-bench Multilingual | — | 39.7% |
| WeirdML | — | 52.7% |
| AlgoTune | — | 1.38 |
Agentic & Tool Use Not comparable
DeepSeek-V3.1-Terminus: —, GPT-5 Mini: 31.1 (#70)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Mini |
|---|---|---|
| Terminal-Bench | — | 34.8% |
| Berkeley Function Calling Leaderboard | — | 55.5% |
| Vending-Bench 2 | — | -31.18 |
Reasoning DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 26.4 (#133), GPT-5 Mini: 23.9 (#168)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Mini |
|---|---|---|
| Kagi LLM Benchmark | 57.4% | 70.3% |
| CritPt | 1.7% | 0% |
| LMArena Hard Prompts | 1426 | 1380 |
| DTBench | 81.3% | 80.5% |
| LMCA | 28.6% | 34.2% |
| ARC-AGI-2 | — | 4.4% |
| ARC-AGI-1 | — | 54.3% |
| Chess Puzzles | — | 30% |
| EnigmaEval | — | 8.2% |
| Mystery Game Puzzles | — | 10% |
| Epoch Capabilities Index | — | 145.52 |
| ForecastBench | — | 61 |
Math GPT-5 Mini leads
DeepSeek-V3.1-Terminus: 38.5 (#137), GPT-5 Mini: 46.7 (#69)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Mini |
|---|---|---|
| LMArena Math | 1402 | 1378 |
| FrontierMath (Tiers 1-3) | — | 46.7% |
| FrontierMath Tier 4 | — | 12.2% |
| OTIS Mock AIME 2024-2025 | — | 86.7% |
| ProofBench | — | 9% |
| Omni-MATH | — | 72.2% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 27.2% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge Not comparable
DeepSeek-V3.1-Terminus: —, GPT-5 Mini: 45.6 (#86)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Mini |
|---|---|---|
| GPQA Diamond | — | 75% |
| Humanity's Last Exam | — | 19.4% |
| SimpleQA Verified | — | 21.6% |
| MMLU-Pro | — | 83.5% |
| Confabulations | — | 13.3% |
| Vectara Hallucination Rate | — | 12.9% |
| GPQA (HELM) | — | 75.6% |
| LMArena Expert | — | 1379 |
Multimodal Not comparable
DeepSeek-V3.1-Terminus: —, GPT-5 Mini: 35.6 (#85)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Mini |
|---|---|---|
| LMArena Vision | — | 1202 |
| VPCT | — | 40.2% |
Multilingual DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 52.1 (#92), GPT-5 Mini: 48.9 (#137)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Mini |
|---|---|---|
| LMArena Non-English | 1407 | 1363 |
| LMArena Russian | 1436 | 1362 |
| LMArena Chinese | — | 1385 |
| LMArena French | — | 1386 |
| LMArena German | — | 1366 |
| LMArena Japanese | — | 1341 |
| LMArena Korean | — | 1308 |
| LMArena Spanish | — | 1355 |
Instruction Following GPT-5 Mini leads
DeepSeek-V3.1-Terminus: 74.0 (#106), GPT-5 Mini: 76.2 (#46)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Mini |
|---|---|---|
| LMArena Instruction Following | 1404 | 1357 |
| IFEval | — | 92.7% |
Long Context DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 43.4 (#97), GPT-5 Mini: 41.9 (#132)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Mini |
|---|---|---|
| LMArena Longer Query | 1421 | 1355 |
| Fiction.LiveBench | — | 69.4% |
Writing & Preference DeepSeek-V3.1-Terminus leads
DeepSeek-V3.1-Terminus: 61.0 (#92), GPT-5 Mini: 55.2 (#148)
| Benchmark | DeepSeek-V3.1-Terminus | GPT-5 Mini |
|---|---|---|
| LMArena Text | 1419 | 1373 |
| LMArena Creative Writing | 1403 | 1325 |
| LMArena Multi-Turn | 1411 | 1363 |
| Short-Story Creative Writing | — | 83.1% |
| EQ-Bench Creative Writing | — | 1313 |
| WildBench | — | 85.5% |
Frequently asked questions
Is DeepSeek-V3.1-Terminus better than GPT-5 Mini?
DeepSeek-V3.1-Terminus is the stronger model overall, scoring 43.1 to 41.8 on the Noometry Index.
Which is cheaper, DeepSeek-V3.1-Terminus or GPT-5 Mini?
DeepSeek-V3.1-Terminus is cheaper. It lists at $0.27 per million input tokens and $1 per million output tokens; GPT-5 Mini lists at $0.25 and $2.
Is DeepSeek-V3.1-Terminus or GPT-5 Mini better for coding?
DeepSeek-V3.1-Terminus scores higher on coding benchmarks: 42.0 versus 40.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Mini does, with 400K tokens against 164K.
How many benchmarks do DeepSeek-V3.1-Terminus and GPT-5 Mini share?
16 benchmarks have published results for both models. DeepSeek-V3.1-Terminus has 16 scored results on Noometry and GPT-5 Mini has 60.