DeepSeek V4 Pro 0423 vs GPT-5.5
GPT-5.5 leads DeepSeek V4 Pro 0423 on coding, 71.3 to 38.7 on the Vibe Coding Index, and DeepSeek V4 Pro 0423 costs 10.1x less per output token ($2.976 vs $30.00 per million). Updated October 22, 2026; 83 benchmarks have a result for both.
DeepSeek V4 Pro 0423 or GPT-5.5: which to pick
Pick GPT-5.5 for coding: it scores 71.3 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423.
Pick DeepSeek V4 Pro 0423 for output tokens 10.1x cheaper ($2.976 vs $30.00 per million) and a lower cost per solved task ($0.57 against $1.65).
- Shared results
- 83
- DeepSeek V4 Pro 0423 only
- 1
- GPT-5.5 only
- 51
- Like-for-like categories
- 8 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
GPT-5.5
GPT-5.5 scores 71.3 against 38.7.
5 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.5
GPT-5.5 leads, 38.4 against 30.4.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.5
GPT-5.5 leads, 36.4 against 26.3.
Same board, both measured
Reasoning
GPQA Diamond
GPT-5.5
GPT-5.5 leads, 93.2% against 89.4%.
Same board, both measured
Knowledge
MMLU Pro
GPT-5.5
GPT-5.5 leads, 88.1% against 87.3%.
Same board, both measured
Web design
Design Arena: websites
GPT-5.5
GPT-5.5 leads, 1256 against 1234.
Same board, both measured
Long documents
Listed context window
GPT-5.5
GPT-5.5 takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 $0.0028, GPT-5.5 $0.020 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 $0.57, GPT-5.5 $1.65.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, GPT-5.5 71.3 from 5 benchmarks.
- Widest category
- Math, 28.4 points apart on average over 6 shared benchmarks.
- Widest single gap
- FrontierMath, tier 4: DeepSeek V4 Pro 0423 2.4% against GPT-5.5 72.5%, 70.1 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $2.976 against GPT-5.5 $30.00 per million output tokens, 10.1x apart.
- Context window
- DeepSeek V4 Pro 0423 takes 1.05M 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 V4 Pro 0423 | Bars | GPT-5.5 | Ties |
|---|---|---|---|---|
| Coding | 1 | 15 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 1 | 8 | ||
| Reasoning | 1 | 7 | ||
| Math | 1 | 5 | ||
| Knowledge | 1 | 10 | ||
| Web design | 1 | 7 | ||
| General | 1 | 22 | ||
| All shared boards | 7 | 76 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index115
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5.5 | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 85.1% | Only GPT-5.5 measured |
| LMArena WebDev | 1447 | 1513 | GPT-5.5 |
| AA Coding Index | 59.4 | 74.9 | GPT-5.5 |
| LiveCodeBench (Vals AI) | 87.5% | 85.3% | DeepSeek V4 Pro 0423 |
| SWE-bench Verified (Vals AI) | 77.4% | 82.6% | GPT-5.5 |
| Terminal-Bench 4 (Vals AI) | 11.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Vibe Code Bench (Vals AI) | 49.9% | 69.8% | GPT-5.5 |
| Code migration (Vals AI) | 26.2% | 45.2% | GPT-5.5 |
| ProgramBench (Vals AI) | 0.0% | 0.5% | GPT-5.5 |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | 76.4% | GPT-5.5 |
| SkillsBench (Vals AI) | 51.3% | 62.5% | GPT-5.5 |
| 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 | 17.6% | 43.0% | GPT-5.5 |
| GSO-Bench | Not measured | 40.2% | Only GPT-5.5 measured |
| WeirdML | 48.9% | 84.9% | GPT-5.5 |
| MirrorCode | Not measured | 10.0% | Only GPT-5.5 measured |
| ALE-Bench | 1006.1 | 1943.0 | GPT-5.5 |
| LMArena Text: coding | 1470 | 1494 | GPT-5.5 |
| LMArena Text: software and IT | 1469 | 1493 | GPT-5.5 |
| LMArena WebDev: React | 1441 | 1506 | GPT-5.5 |
| LMArena WebDev: HTML | 1435 | 1543 | GPT-5.5 |
| LMArena WebDev: from an image | Not measured | 1532 | Only GPT-5.5 measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 38.4 | GPT-5.5 |
| Epoch Capabilities Index | 149.1 | 159.7 | GPT-5.5 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index18
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | 36.4 | GPT-5.5 |
| Design Arena: agents building web apps | 1000 | 1117 | GPT-5.5 |
| Design Arena: agents building full-stack apps | 948 | 1089 | GPT-5.5 |
| Design Arena: agents building mobile apps | Not measured | 1142 | Only GPT-5.5 measured |
| Design Arena: agents building Android apps | Not measured | 1164 | Only GPT-5.5 measured |
| Design Arena: agents building Godot games | 1059 | 1173 | GPT-5.5 |
| Design Arena: agents building games | Not measured | 1179 | Only GPT-5.5 measured |
| Design Arena: HTML slides | Not measured | 1017 | Only GPT-5.5 measured |
| Design Arena: PowerPoint slides | Not measured | 1084 | 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) | 44.1% | 51.8% | GPT-5.5 |
| Tax Agent Bench (Vals AI) | 25.2% | 21.8% | DeepSeek V4 Pro 0423 |
| Legal Research Bench (Vals AI) | 23.1% | 40.4% | GPT-5.5 |
| Excel Modeling Benchmark (Vals AI) | 51.6% | 64.5% | GPT-5.5 |
| 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 | $3,285 | $7,524 | GPT-5.5 |
| Neither model has a result on the other 16 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | 93.2% | GPT-5.5 |
| 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) | 90.9% | 90.7% | DeepSeek V4 Pro 0423 |
| 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 | 19.0% | 63.0% | GPT-5.5 |
| Mystery game puzzles | 17.0% | 56.0% | GPT-5.5 |
| EBR-bench | Not measured | 34.3% | Only GPT-5.5 measured |
| LMArena Text: hard prompts | 1461 | 1489 | GPT-5.5 |
| LMArena Text: expert | 1463 | 1507 | GPT-5.5 |
| LMArena Text: hard prompts in English | 1466 | 1483 | GPT-5.5 |
| 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 | 53.5% | 88.8% | GPT-5.5 |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5.5 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 96.7% | 84.4% | DeepSeek V4 Pro 0423 |
| FrontierMath, tiers 1 to 3 | 45.3% | 85.3% | GPT-5.5 |
| FrontierMath, tier 4 | 2.4% | 72.5% | GPT-5.5 |
| ProofBench | 16.0% | 50.0% | GPT-5.5 |
| LMArena Text: math | 1443 | 1487 | GPT-5.5 |
| LMArena Text: mathematical work | 1455 | 1496 | GPT-5.5 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5.5 | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | 88.1% | GPT-5.5 |
| LegalBench (Vals AI) | 80.3% | 86.5% | GPT-5.5 |
| CaseLaw (Vals AI) | 59.4% | 66.2% | GPT-5.5 |
| CorpFin (Vals AI) | 61.4% | 68.4% | GPT-5.5 |
| TaxEval (Vals AI) | 72.1% | 75.0% | GPT-5.5 |
| MortgageTax (Vals AI) | Not measured | 68.8% | Only GPT-5.5 measured |
| MedCode (Vals AI) | 40.5% | 49.1% | GPT-5.5 |
| SimpleQA Verified | 47.0% | 63.0% | GPT-5.5 |
| LMArena Text: science | 1470 | 1485 | GPT-5.5 |
| LMArena Text: medicine and healthcare | 1466 | 1465 | DeepSeek V4 Pro 0423 |
| LMArena Text: legal and government | 1464 | 1491 | GPT-5.5 |
| LMArena Text: business and finance | 1443 | 1478 | GPT-5.5 |
| 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 index17
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1234 | 1256 | GPT-5.5 |
| Design Arena: UI components | 1219 | 1257 | GPT-5.5 |
| Design Arena: game dev | 1228 | 1293 | GPT-5.5 |
| Design Arena: data viz | 1203 | 1264 | GPT-5.5 |
| Design Arena: 3D | 1253 | 1197 | DeepSeek V4 Pro 0423 |
| Design Arena: all code categories | 1240 | 1260 | GPT-5.5 |
| Design Arena: SVG | 1139 | 1234 | GPT-5.5 |
| Design Arena: ASCII art | 1149 | 1255 | GPT-5.5 |
| LiveBench design | Not measured | 70.3% | Only GPT-5.5 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index122
| Benchmark | DeepSeek V4 Pro 0423 | GPT-5.5 | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 81.6% | Only GPT-5.5 measured |
| Public Benefits Bench (Vals AI) | 62.9% | 60.9% | DeepSeek V4 Pro 0423 |
| SAGE (Vals AI) | Not measured | 51.5% | Only GPT-5.5 measured |
| MedScribe (Vals AI) | 75.1% | 86.9% | GPT-5.5 |
| 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 | 90.7% | 96.0% | GPT-5.5 |
| LMCA | 41.2% | 54.3% | GPT-5.5 |
| Surface Evolver Bench | 40.0% | 88.1% | GPT-5.5 |
| Furniture assembly | Not measured | 44.2% | Only GPT-5.5 measured |
| ExploitBench | Not measured | 47.4% | Only GPT-5.5 measured |
| CL-bench Life | 13.5% | 22.2% | GPT-5.5 |
| LMArena Text | 1451 | 1471 | GPT-5.5 |
| LMArena Text: instruction following | 1445 | 1478 | GPT-5.5 |
| LMArena Text: creative writing | 1446 | 1455 | GPT-5.5 |
| LMArena Vision | Not measured | 1297 | Only GPT-5.5 measured |
| LMArena Text: English | 1459 | 1470 | GPT-5.5 |
| LMArena Text: non-English | 1439 | 1467 | GPT-5.5 |
| LMArena Text: Chinese | 1481 | 1534 | GPT-5.5 |
| LMArena Text: German | 1458 | 1480 | GPT-5.5 |
| LMArena Text: Japanese | 1446 | 1499 | GPT-5.5 |
| LMArena Text: Korean | 1409 | 1460 | GPT-5.5 |
| LMArena Text: French | 1473 | 1485 | GPT-5.5 |
| LMArena Text: Spanish | 1457 | 1468 | GPT-5.5 |
| LMArena Text: Russian | 1453 | 1473 | GPT-5.5 |
| LMArena Text: Polish | 1461 | 1465 | GPT-5.5 |
| LMArena Text: multi-turn | 1468 | 1476 | GPT-5.5 |
| LMArena Text: longer queries | 1458 | 1484 | GPT-5.5 |
| LMArena Text: writing and language | 1447 | 1466 | GPT-5.5 |
| LMArena Text: entertainment and media | 1431 | 1449 | GPT-5.5 |
| 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 V4 Pro 0423 | 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 V4 Pro 0423 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Pro 0423 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Pro 0423 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 V4 Pro 0423 | OpenAI GPT-5.5 |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | April 24, 2026 | April 24, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.358 | $5.00 |
| Output $/Mtok | $2.976 | $30.00 |
| Coding index | 38.7 | 71.3 |
| Independent measurements behind it | 5 | 5 |
| Cost per solved task | $0.57 | $1.65 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than GPT-5.5 for coding?
Pick GPT-5.5 for coding: it scores 71.3 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423. Pick DeepSeek V4 Pro 0423 for output tokens 10.1x cheaper ($2.976 vs $30.00 per million) and a lower cost per solved task ($0.57 against $1.65).
Which is cheaper, DeepSeek V4 Pro 0423 or GPT-5.5?
DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; GPT-5.5 costs $5.00 and $30.00. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and GPT-5.5 $1.65.
Which has the bigger context window, DeepSeek V4 Pro 0423 or GPT-5.5?
GPT-5.5, with 1.05M tokens against 1.05M.
How many benchmarks is the DeepSeek V4 Pro 0423 vs GPT-5.5 comparison based on?
83 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0423's coding index rests on 5 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.