Model comparison
DeepSeek-V3.1 vs GPT-5.5 Instant
DeepSeek-V3.1 and GPT-5.5 Instant score almost the same on the Noometry Index (42.8 vs 42.7), so choose on price, context window or the category you care about most.
Last verified . 18 shared benchmarks.
Summary
- They share 18 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 2 categories and GPT-5.5 Instant in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek-V3.1 leads 38.9 to 26.5.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1 | GPT-5.5 Instant | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.8 | 42.7 |
| Released | 2025-08-21 | 2026-05-05 |
| Weights | Open | Proprietary |
| Context window | 164K | — |
| Max output | 8K | — |
| Input $ / M tokens | $0.25 | — |
| Output $ / M tokens | $0.95 | — |
| Results tracked | 27 | 27 |
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Category by category
Coding GPT-5.5 Instant leads
DeepSeek-V3.1: 40.3 (#144), GPT-5.5 Instant: 44.3 (#74)
| Benchmark | DeepSeek-V3.1 | GPT-5.5 Instant |
|---|---|---|
| LMArena Coding | 1417 | 1433 |
| SciCode | — | 48.6% |
| WeirdML | 38.4% | — |
Reasoning DeepSeek-V3.1 leads
DeepSeek-V3.1: 27.9 (#110), GPT-5.5 Instant: 24.9 (#155)
| Benchmark | DeepSeek-V3.1 | GPT-5.5 Instant |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1426 |
| Epoch Capabilities Index | 139.92 | 142.52 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| CritPt | — | 0% |
| Chess Puzzles | — | 12% |
| DTBench | 82.7% | — |
| LMCA | 24.3% | — |
| ForecastBench | 58 | — |
Math DeepSeek-V3.1 leads
DeepSeek-V3.1: 38.9 (#122), GPT-5.5 Instant: 26.5 (#259)
| Benchmark | DeepSeek-V3.1 | GPT-5.5 Instant |
|---|---|---|
| LMArena Math | 1420 | 1420 |
| FrontierMath (Tiers 1-3) | — | 26.3% |
| FrontierMath Tier 4 | — | 2.4% |
| OTIS Mock AIME 2024-2025 | — | 68.1% |
Knowledge GPT-5.5 Instant leads
DeepSeek-V3.1: 43.7 (#90), GPT-5.5 Instant: 48.9 (#74)
| Benchmark | DeepSeek-V3.1 | GPT-5.5 Instant |
|---|---|---|
| LMArena Expert | 1405 | 1409 |
| GPQA Diamond | — | 82.5% |
| Vectara Hallucination Rate | 5.5% | — |
Multimodal Not comparable
DeepSeek-V3.1: —, GPT-5.5 Instant: 40.0 (#52)
| Benchmark | DeepSeek-V3.1 | GPT-5.5 Instant |
|---|---|---|
| LMArena Vision | — | 1250 |
| LMArena Document | — | 1403 |
Multilingual GPT-5.5 Instant leads
DeepSeek-V3.1: 51.6 (#106), GPT-5.5 Instant: 52.8 (#80)
| Benchmark | DeepSeek-V3.1 | GPT-5.5 Instant |
|---|---|---|
| LMArena Non-English | 1400 | 1417 |
| LMArena Chinese | 1469 | 1456 |
| LMArena French | 1447 | 1428 |
| LMArena German | 1411 | 1411 |
| LMArena Japanese | 1378 | 1408 |
| LMArena Korean | 1337 | 1392 |
| LMArena Russian | 1405 | 1431 |
| LMArena Spanish | 1431 | 1429 |
Instruction Following Too close to call
DeepSeek-V3.1: 73.9 (#110), GPT-5.5 Instant: 74.2 (#100)
| Benchmark | DeepSeek-V3.1 | GPT-5.5 Instant |
|---|---|---|
| LMArena Instruction Following | 1400 | 1406 |
Long Context GPT-5.5 Instant leads
DeepSeek-V3.1: 36.3 (#232), GPT-5.5 Instant: 43.4 (#96)
| Benchmark | DeepSeek-V3.1 | GPT-5.5 Instant |
|---|---|---|
| LMArena Longer Query | 1422 | 1422 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference GPT-5.5 Instant leads
DeepSeek-V3.1: 60.3 (#98), GPT-5.5 Instant: 61.8 (#85)
| Benchmark | DeepSeek-V3.1 | GPT-5.5 Instant |
|---|---|---|
| LMArena Text | 1420 | 1419 |
| LMArena Creative Writing | 1401 | 1419 |
| LMArena Multi-Turn | 1408 | 1433 |
| EQ-Bench Creative Writing | 1436 | — |
Frequently asked questions
Is DeepSeek-V3.1 better than GPT-5.5 Instant?
DeepSeek-V3.1 and GPT-5.5 Instant score almost the same on the Noometry Index (42.8 vs 42.7), so choose on price, context window or the category you care about most.
Is DeepSeek-V3.1 or GPT-5.5 Instant better for coding?
GPT-5.5 Instant scores higher on coding benchmarks: 44.3 versus 40.3 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.1 and GPT-5.5 Instant share?
18 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and GPT-5.5 Instant has 27.