DeepSeek V3.1 vs GPT-5.5
GPT-5.5 leads DeepSeek V3.1 on the Artificial Analysis Intelligence Index, 38.4 to 13.7, and DeepSeek V3.1 costs 20.0x less per output token ($1.50 vs $30.00 per million). Updated October 21, 2026; 11 benchmarks have a result for both.
vs boards won of 11 shared
DeepSeek V3.1 or GPT-5.5: which to pick
Only GPT-5.5 has a coding score, 71.3 on the Vibe Coding Index; DeepSeek V3.1 has none, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for output tokens 20.0x cheaper ($1.50 vs $30.00 per million).
Model ADeepSeek V3.1DeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGPT-5.5OpenAI71.3/1005 independent benchmarks$1.65 per solved task
- Shared results
- 11
- DeepSeek V3.1 only
- 0
- GPT-5.5 only
- 123
- Like-for-like categories
- 4 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GPT-5.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.5
GPT-5.5 leads, 38.4 against 13.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.5 has a AA Agentic Index result, 36.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.5 has a GPQA Diamond result, 93.2%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.5 has a MMLU Pro result, 88.1%.
Directional only
Web design
Design Arena: websites
GPT-5.5
GPT-5.5 leads, 1256 against 1123.
Same board, both measured
Long documents
Listed context window
GPT-5.5
GPT-5.5 takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.1
DeepSeek V3.1 $0.0013, GPT-5.5 $0.020 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.5 has the number, $1.65.
Directional only
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, GPT-5.5 71.3 from 5 benchmarks.
- Widest category
- Reasoning, 35.6 points apart on average over 1 shared benchmark.
- Widest single gap
- Kagi LLM Benchmark: DeepSeek V3.1 53.2% against GPT-5.5 88.8%, 35.6 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against GPT-5.5 $30.00 per million output tokens, 20.0x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, GPT-5.5 1.05M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V3.1 | Bars | GPT-5.5 | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Web design | 0 | 7 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 11 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index23 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.5 | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 85.1% | Only GPT-5.5 measured |
| LMArena WebDev | Not measured | 1513 | Only GPT-5.5 measured |
| AA Coding Index | Not measured | 74.9 | Only GPT-5.5 measured |
| LiveCodeBench (Vals AI) | Not measured | 85.3% | Only GPT-5.5 measured |
| SWE-bench Verified (Vals AI) | Not measured | 82.6% | Only GPT-5.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 69.8% | Only GPT-5.5 measured |
| Code migration (Vals AI) | Not measured | 45.2% | Only GPT-5.5 measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GPT-5.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 76.4% | Only GPT-5.5 measured |
| SkillsBench (Vals AI) | Not measured | 62.5% | Only GPT-5.5 measured |
| SciCode | Not measured | 56.1% | Only GPT-5.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 60.6% | Only GPT-5.5 measured |
| Terminal-Bench | Not measured | 84.7% | Only GPT-5.5 measured |
| FrontierCode | Not measured | 43.0% | Only GPT-5.5 measured |
| GSO-Bench | Not measured | 40.2% | Only GPT-5.5 measured |
| WeirdML | Not measured | 84.9% | Only GPT-5.5 measured |
| MirrorCode | Not measured | 10.0% | Only GPT-5.5 measured |
| ALE-Bench | Not measured | 1943.0 | Only GPT-5.5 measured |
| LMArena Text: coding | Not measured | 1494 | Only GPT-5.5 measured |
| LMArena Text: software and IT | Not measured | 1493 | Only GPT-5.5 measured |
| LMArena WebDev: React | Not measured | 1506 | Only GPT-5.5 measured |
| LMArena WebDev: HTML | Not measured | 1543 | Only GPT-5.5 measured |
| LMArena WebDev: from an image | Not measured | 1532 | Only GPT-5.5 measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 | GPT-5.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 38.4 | GPT-5.5 |
| Epoch Capabilities Index | Not measured | 159.7 | Only GPT-5.5 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index25 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.5 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 36.4 | Only GPT-5.5 measured |
| Design Arena: agents building web apps | Not measured | 1117 | Only GPT-5.5 measured |
| Design Arena: agents building full-stack apps | Not measured | 1089 | Only GPT-5.5 measured |
| Design Arena: agents building mobile apps | Not measured | 1143 | Only GPT-5.5 measured |
| Design Arena: agents building Android apps | Not measured | 1163 | Only GPT-5.5 measured |
| Design Arena: agents building Godot games | Not measured | 1173 | Only GPT-5.5 measured |
| Design Arena: agents building games | Not measured | 1179 | Only GPT-5.5 measured |
| Design Arena: HTML slides | Not measured | 1067 | Only GPT-5.5 measured |
| Design Arena: PowerPoint slides | Not measured | 1109 | Only GPT-5.5 measured |
| LiveBench research | Not measured | 61.6% | Only GPT-5.5 measured |
| LiveBench browser | Not measured | 48.0% | Only GPT-5.5 measured |
| LiveBench long agentic | Not measured | 44.6% | Only GPT-5.5 measured |
| SREBench (Vals AI) | Not measured | 3.8% | Only GPT-5.5 measured |
| Finance Agent (Vals AI) | Not measured | 51.8% | Only GPT-5.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 21.8% | Only GPT-5.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 40.4% | Only GPT-5.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 64.5% | Only GPT-5.5 measured |
| Time Horizon Index (Vals AI) | Not measured | 8.3% | Only GPT-5.5 measured |
| τ²-Bench Telecom | Not measured | 93.9% | Only GPT-5.5 measured |
| APEX-Agents | Not measured | 55.1% | Only GPT-5.5 measured |
| Remote Labor Index | Not measured | 6.3% | Only GPT-5.5 measured |
| OSWorld 2.0 | Not measured | 13.0% | Only GPT-5.5 measured |
| DeepResearch Bench | Not measured | 54.0% | Only GPT-5.5 measured |
| PostTrainBench | Not measured | 27.2% | Only GPT-5.5 measured |
| Vending-Bench 2 | Not measured | $7,524 | Only GPT-5.5 measured |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 | GPT-5.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 93.2% | Only GPT-5.5 measured |
| LiveBench reasoning | Not measured | 89.7% | Only GPT-5.5 measured |
| MMMU Pro (Vals AI) | Not measured | 88.3% | Only GPT-5.5 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 93.5% | Only GPT-5.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 45.8% | Only GPT-5.5 measured |
| GPQA Diamond (Epoch AI run) | Not measured | 90.7% | Only GPT-5.5 measured |
| ARC-AGI | Not measured | 95.0% | Only GPT-5.5 measured |
| ARC-AGI-2 | Not measured | 85.0% | Only GPT-5.5 measured |
| SimpleBench | Not measured | 69.0% | Only GPT-5.5 measured |
| Chess puzzles | Not measured | 63.0% | Only GPT-5.5 measured |
| Mystery game puzzles | Not measured | 56.0% | Only GPT-5.5 measured |
| EBR-bench | Not measured | 34.3% | Only GPT-5.5 measured |
| LMArena Text: hard prompts | Not measured | 1489 | Only GPT-5.5 measured |
| LMArena Text: expert | Not measured | 1507 | Only GPT-5.5 measured |
| LMArena Text: hard prompts in English | Not measured | 1483 | Only GPT-5.5 measured |
| LMArena Vision: diagrams | Not measured | 1325 | Only GPT-5.5 measured |
| LMArena Vision: homework | Not measured | 1332 | Only GPT-5.5 measured |
| Kagi LLM Benchmark | 53.2% | 88.8% | GPT-5.5 |
| Neither model has a result on the other 4 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.5 | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 84.4% | Only GPT-5.5 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 85.3% | Only GPT-5.5 measured |
| FrontierMath, tier 4 | Not measured | 72.5% | Only GPT-5.5 measured |
| ProofBench | Not measured | 50.0% | Only GPT-5.5 measured |
| LMArena Text: math | Not measured | 1487 | Only GPT-5.5 measured |
| LMArena Text: mathematical work | Not measured | 1496 | Only GPT-5.5 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 88.1% | Only GPT-5.5 measured |
| LegalBench (Vals AI) | Not measured | 86.5% | Only GPT-5.5 measured |
| CaseLaw (Vals AI) | Not measured | 66.2% | Only GPT-5.5 measured |
| CorpFin (Vals AI) | Not measured | 68.4% | Only GPT-5.5 measured |
| TaxEval (Vals AI) | Not measured | 75.0% | Only GPT-5.5 measured |
| MortgageTax (Vals AI) | Not measured | 68.8% | Only GPT-5.5 measured |
| MedCode (Vals AI) | Not measured | 49.1% | Only GPT-5.5 measured |
| SimpleQA Verified | Not measured | 63.0% | Only GPT-5.5 measured |
| LMArena Text: science | Not measured | 1485 | Only GPT-5.5 measured |
| LMArena Text: medicine and healthcare | Not measured | 1465 | Only GPT-5.5 measured |
| LMArena Text: legal and government | Not measured | 1491 | Only GPT-5.5 measured |
| LMArena Text: business and finance | Not measured | 1478 | Only GPT-5.5 measured |
| FACTS Benchmark Suite | Not measured | 66.7% | Only GPT-5.5 measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | DeepSeek V3.1 | GPT-5.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1123 | 1256 | GPT-5.5 |
| Design Arena: UI components | 1085 | 1260 | GPT-5.5 |
| Design Arena: game dev | 1084 | 1294 | GPT-5.5 |
| Design Arena: data viz | 1095 | 1265 | GPT-5.5 |
| Design Arena: 3D | 1077 | 1198 | GPT-5.5 |
| Design Arena: all code categories | 1112 | 1260 | GPT-5.5 |
| Design Arena: SVG | 967 | 1234 | GPT-5.5 |
| Design Arena: ASCII art | Not measured | 1255 | Only GPT-5.5 measured |
| LiveBench design | Not measured | 70.3% | Only GPT-5.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.1 | GPT-5.5 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 81.6% | Only GPT-5.5 measured |
| Public Benefits Bench (Vals AI) | Not measured | 60.9% | Only GPT-5.5 measured |
| SAGE (Vals AI) | Not measured | 51.5% | Only GPT-5.5 measured |
| MedScribe (Vals AI) | Not measured | 86.9% | Only GPT-5.5 measured |
| Vals Multimodal Index | Not measured | 68.1% | Only GPT-5.5 measured |
| IFBench | Not measured | 75.9% | Only GPT-5.5 measured |
| Long context reasoning (AA-LCR) | Not measured | 84.3% | Only GPT-5.5 measured |
| DTBench | 82.7% | 96.0% | GPT-5.5 |
| LMCA | 24.3% | 54.3% | GPT-5.5 |
| Surface Evolver Bench | Not measured | 88.1% | Only GPT-5.5 measured |
| Furniture assembly | Not measured | 44.2% | Only GPT-5.5 measured |
| ExploitBench | Not measured | 47.4% | Only GPT-5.5 measured |
| CL-bench Life | Not measured | 22.2% | Only GPT-5.5 measured |
| LMArena Text | Not measured | 1471 | Only GPT-5.5 measured |
| LMArena Text: instruction following | Not measured | 1478 | Only GPT-5.5 measured |
| LMArena Text: creative writing | Not measured | 1455 | Only GPT-5.5 measured |
| LMArena Vision | Not measured | 1297 | Only GPT-5.5 measured |
| LMArena Text: English | Not measured | 1470 | Only GPT-5.5 measured |
| LMArena Text: non-English | Not measured | 1467 | Only GPT-5.5 measured |
| LMArena Text: Chinese | Not measured | 1534 | Only GPT-5.5 measured |
| LMArena Text: German | Not measured | 1480 | Only GPT-5.5 measured |
| LMArena Text: Japanese | Not measured | 1499 | Only GPT-5.5 measured |
| LMArena Text: Korean | Not measured | 1460 | Only GPT-5.5 measured |
| LMArena Text: French | Not measured | 1485 | Only GPT-5.5 measured |
| LMArena Text: Spanish | Not measured | 1468 | Only GPT-5.5 measured |
| LMArena Text: Russian | Not measured | 1473 | Only GPT-5.5 measured |
| LMArena Text: Polish | Not measured | 1465 | Only GPT-5.5 measured |
| LMArena Text: multi-turn | Not measured | 1476 | Only GPT-5.5 measured |
| LMArena Text: longer queries | Not measured | 1484 | Only GPT-5.5 measured |
| LMArena Text: writing and language | Not measured | 1466 | Only GPT-5.5 measured |
| LMArena Text: entertainment and media | Not measured | 1449 | Only GPT-5.5 measured |
| LMArena Vision: English | Not measured | 1292 | Only GPT-5.5 measured |
| LMArena Vision: Chinese | Not measured | 1360 | Only GPT-5.5 measured |
| LMArena Vision: reading text | Not measured | 1306 | Only GPT-5.5 measured |
| LMArena Vision: humor | Not measured | 1293 | Only GPT-5.5 measured |
| LMArena Vision: recognizing things | Not measured | 1231 | Only GPT-5.5 measured |
| LMArena Vision: creative writing | Not measured | 1301 | Only GPT-5.5 measured |
| LMArena Document | Not measured | 1486 | Only GPT-5.5 measured |
| Neither model has a result on the other 8 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.5 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
DeepSeek V3.1$0.0013Fits in one request
GPT-5.5$0.020Fits in one request
DeepSeek V3.1 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.1$0.032Fits in one request
GPT-5.5$0.340Fits in one request
DeepSeek V3.1 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.1$0.125Exceeds the context window
GPT-5.5$1.30Fits in one request
DeepSeek V3.1 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V3.1 | OpenAI GPT-5.5 |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 21, 2025 | April 24, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.55 | $5.00 |
| Output $/Mtok | $1.50 | $30.00 |
| Coding index | Not benchmarked | 71.3 |
| Independent measurements behind it | 0 | 5 |
| Cost per solved task | Not computable | $1.65 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 better than GPT-5.5 for coding?
Only GPT-5.5 has a coding score, 71.3 on the Vibe Coding Index; DeepSeek V3.1 has none, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for output tokens 20.0x cheaper ($1.50 vs $30.00 per million).
Which is cheaper, DeepSeek V3.1 or GPT-5.5?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; GPT-5.5 costs $5.00 and $30.00. Cost per solved task is known for GPT-5.5 only.
Which has the bigger context window, DeepSeek V3.1 or GPT-5.5?
GPT-5.5, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.1 vs GPT-5.5 comparison based on?
11 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and GPT-5.5's on 5. Where only one model has a result, the page says so instead of naming a winner.