DeepSeek V4 Pro 0813 vs GPT-5.6 Terra
Updated October 21, 2026. 76 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-5.6 Terra for coding: it scores 69.0 on the Vibe Coding Index against 54.6 for DeepSeek V4 Pro 0813.
Pick DeepSeek V4 Pro 0813 when output tokens cost 6.1x less, or when a solved task costs $0.10 against $0.42.
- Shared results
- 76
- DeepSeek V4 Pro 0813 only
- 2
- GPT-5.6 Terra only
- 48
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
GPT-5.6 Terra
GPT-5.6 Terra scores 69.0 against 54.6.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 42.1 against 36.0.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 43.2 against 41.3.
Same board, both measured
Reasoning
GPQA Diamond
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 92.4% against 90.9%.
Same board, both measured
Knowledge
MMLU Pro
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 87.0% against 86.7%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.6 Terra has a Design Arena: websites result, 1269.
Directional only
General
Vals Index
No call
Only DeepSeek V4 Pro 0813 has a Vals Index result, 53.0%.
Directional only
Long documents
Listed context window
GPT-5.6 Terra
GPT-5.6 Terra takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 $0.0016, GPT-5.6 Terra $0.0080 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 $0.10, GPT-5.6 Terra $0.42.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Pro 0813 54.6 from 1 benchmark, GPT-5.6 Terra 69.0 from 2 benchmarks.
- Widest category
- Math, 16.7 points apart on average over 6 shared benchmarks.
- Widest single gap
- FrontierMath, tier 4: DeepSeek V4 Pro 0813 26.8% against GPT-5.6 Terra 70.7%, 43.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0813 $1.98 against GPT-5.6 Terra $12.00 per million output tokens, 6.1x apart.
- Context window
- DeepSeek V4 Pro 0813 takes 1.05M tokens, GPT-5.6 Terra 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 0813 | Bars | GPT-5.6 Terra | Ties |
|---|---|---|---|---|
| Coding | 8 | 13 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 8 | ||
| Reasoning | 3 | 8 | ||
| Math | 0 | 6 | ||
| Knowledge | 5 | 5 | ||
| General | 8 | 10 | ||
| All shared boards | 24 | 52 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index813
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 86.2% | Only GPT-5.6 Terra measured |
| LMArena WebDev | 1582 | 1522 | DeepSeek V4 Pro 0813 |
| AA Coding Index | 68.8 | 76.7 | GPT-5.6 Terra |
| LiveCodeBench (Vals AI) | 87.5% | 85.9% | DeepSeek V4 Pro 0813 |
| SWE-bench Verified (Vals AI) | 96.4% | 95.4% | DeepSeek V4 Pro 0813 |
| Terminal-Bench 4 (Vals AI) | 14.1% | 22.7% | GPT-5.6 Terra |
| Vibe Code Bench (Vals AI) | 82.3% | 74.6% | DeepSeek V4 Pro 0813 |
| Code migration (Vals AI) | 41.5% | 47.8% | GPT-5.6 Terra |
| ProgramBench (Vals AI) | 0.0% | 0.5% | GPT-5.6 Terra |
| IOI (Vals AI) | 51.6% | 87.6% | GPT-5.6 Terra |
| Terminal-Bench 2.1 (Vals AI) | 54.7% | 77.5% | GPT-5.6 Terra |
| Vibe Code Bench 1-100 (Vals AI) | 17.5% | 14.8% | DeepSeek V4 Pro 0813 |
| SkillsBench (Vals AI) | 53.8% | 58.9% | GPT-5.6 Terra |
| CyberBench (Vals AI) | 72.9% | 72.1% | DeepSeek V4 Pro 0813 |
| SciCode | 51.0% | 55.0% | GPT-5.6 Terra |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 62.9% | Only GPT-5.6 Terra measured |
| FrontierCode | 28.5% | 41.3% | GPT-5.6 Terra |
| WeirdML | 66.2% | 78.3% | GPT-5.6 Terra |
| ALE-Bench | 1403.2 | 1951.4 | GPT-5.6 Terra |
| LMArena Text: coding | 1469 | 1485 | GPT-5.6 Terra |
| LMArena Text: software and IT | 1462 | 1482 | GPT-5.6 Terra |
| LMArena WebDev: React | 1588 | 1513 | DeepSeek V4 Pro 0813 |
| LMArena WebDev: HTML | 1566 | 1526 | DeepSeek V4 Pro 0813 |
| LMArena WebDev: from an image | Not measured | 1541 | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Intelligence Index | 36.0 | 42.1 | GPT-5.6 Terra |
| Epoch Capabilities Index | 155.6 | 160.1 | GPT-5.6 Terra |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Agentic Index | 41.3 | 43.2 | GPT-5.6 Terra |
| Design Arena: agents building web apps | Not measured | 1120 | Only GPT-5.6 Terra measured |
| Design Arena: agents building full-stack apps | Not measured | 1061 | Only GPT-5.6 Terra measured |
| Design Arena: agents building mobile apps | Not measured | 1112 | Only GPT-5.6 Terra measured |
| Design Arena: agents building Android apps | Not measured | 1066 | Only GPT-5.6 Terra measured |
| Design Arena: agents building games | Not measured | 1082 | Only GPT-5.6 Terra measured |
| Design Arena: HTML slides | Not measured | 1108 | Only GPT-5.6 Terra measured |
| Design Arena: PowerPoint slides | Not measured | 1130 | Only GPT-5.6 Terra measured |
| LiveBench research | Not measured | 60.4% | Only GPT-5.6 Terra measured |
| LiveBench browser | Not measured | 84.0% | Only GPT-5.6 Terra measured |
| LiveBench long agentic | Not measured | 48.4% | Only GPT-5.6 Terra measured |
| SREBench (Vals AI) | 1.9% | 9.5% | GPT-5.6 Terra |
| Finance Agent (Vals AI) | 50.4% | 54.4% | GPT-5.6 Terra |
| Tax Agent Bench (Vals AI) | 26.8% | 30.0% | GPT-5.6 Terra |
| Legal Research Bench (Vals AI) | 40.9% | 41.4% | GPT-5.6 Terra |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Excel Modeling Benchmark (Vals AI) | 52.8% | 66.2% | GPT-5.6 Terra |
| Terminal-Bench-Science (Vals AI) | 4.3% | 10.0% | GPT-5.6 Terra |
| τ²-Bench Telecom | Not measured | 86.3% | Only GPT-5.6 Terra measured |
| APEX-Agents | 47.3% | 58.2% | GPT-5.6 Terra |
| BALROG | Not measured | 53.2% | Only GPT-5.6 Terra measured |
| Vending-Bench 2 | Not measured | $7,343 | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 17 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index38
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| GPQA Diamond | 92.4% | 90.9% | DeepSeek V4 Pro 0813 |
| LiveBench reasoning | Not measured | 90.6% | Only GPT-5.6 Terra measured |
| MMMU Pro (Vals AI) | Not measured | 86.5% | Only GPT-5.6 Terra measured |
| GPQA Diamond (Artificial Analysis run) | 92.8% | 92.5% | DeepSeek V4 Pro 0813 |
| Humanity's Last Exam (Artificial Analysis run) | 41.0% | 42.9% | GPT-5.6 Terra |
| GPQA Diamond (Epoch AI run) | 91.7% | 93.3% | GPT-5.6 Terra |
| ARC-AGI | 90.5% | 96.5% | GPT-5.6 Terra |
| ARC-AGI-2 | 61.3% | 83.9% | GPT-5.6 Terra |
| SimpleBench | Not measured | 48.9% | Only GPT-5.6 Terra measured |
| Chess puzzles | 46.0% | 47.0% | GPT-5.6 Terra |
| Mystery game puzzles | 43.0% | 35.0% | DeepSeek V4 Pro 0813 |
| LMArena Text: hard prompts | 1458 | 1469 | GPT-5.6 Terra |
| LMArena Text: expert | 1459 | 1496 | GPT-5.6 Terra |
| LMArena Text: hard prompts in English | 1463 | 1465 | GPT-5.6 Terra |
| LMArena Vision: diagrams | Not measured | 1295 | Only GPT-5.6 Terra measured |
| LMArena Vision: homework | Not measured | 1312 | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.6% | 99.7% | GPT-5.6 Terra |
| FrontierMath, tiers 1 to 3 | 64.6% | 86.0% | GPT-5.6 Terra |
| FrontierMath, tier 4 | 26.8% | 70.7% | GPT-5.6 Terra |
| ProofBench | 50.0% | 74.0% | GPT-5.6 Terra |
| LMArena Text: math | 1441 | 1465 | GPT-5.6 Terra |
| LMArena Text: mathematical work | 1443 | 1478 | GPT-5.6 Terra |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index55
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| MMLU Pro | 87.0% | 86.7% | DeepSeek V4 Pro 0813 |
| LegalBench (Vals AI) | 82.4% | 85.1% | GPT-5.6 Terra |
| CorpFin (Vals AI) | 65.4% | 65.3% | DeepSeek V4 Pro 0813 |
| TaxEval (Vals AI) | 73.1% | 76.2% | GPT-5.6 Terra |
| MortgageTax (Vals AI) | Not measured | 67.3% | Only GPT-5.6 Terra measured |
| MedCode (Vals AI) | 42.5% | 43.4% | GPT-5.6 Terra |
| SimpleQA Verified | 52.9% | 43.2% | DeepSeek V4 Pro 0813 |
| LMArena Text: science | 1457 | 1452 | DeepSeek V4 Pro 0813 |
| LMArena Text: medicine and healthcare | 1446 | 1430 | DeepSeek V4 Pro 0813 |
| LMArena Text: legal and government | 1459 | 1461 | GPT-5.6 Terra |
| LMArena Text: business and finance | 1442 | 1448 | GPT-5.6 Terra |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1269 | Only GPT-5.6 Terra measured |
| Design Arena: UI components | Not measured | 1259 | Only GPT-5.6 Terra measured |
| Design Arena: game dev | Not measured | 1230 | Only GPT-5.6 Terra measured |
| Design Arena: data viz | Not measured | 1240 | Only GPT-5.6 Terra measured |
| Design Arena: 3D | Not measured | 1135 | Only GPT-5.6 Terra measured |
| Design Arena: all code categories | Not measured | 1241 | Only GPT-5.6 Terra measured |
| Design Arena: SVG | Not measured | 1220 | Only GPT-5.6 Terra measured |
| Design Arena: ASCII art | Not measured | 1177 | Only GPT-5.6 Terra measured |
| LiveBench design | Not measured | 76.0% | Only GPT-5.6 Terra measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index810
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| Vals Index | 53.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LiveBench data analysis | Not measured | 79.3% | Only GPT-5.6 Terra measured |
| Public Benefits Bench (Vals AI) | Not measured | 62.4% | Only GPT-5.6 Terra measured |
| SAGE (Vals AI) | Not measured | 47.0% | Only GPT-5.6 Terra measured |
| MedScribe (Vals AI) | 80.2% | 82.9% | GPT-5.6 Terra |
| Vals Multimodal Index | Not measured | 65.1% | Only GPT-5.6 Terra measured |
| IFBench | Not measured | 71.2% | Only GPT-5.6 Terra measured |
| Long context reasoning (AA-LCR) | 80.3% | 83.0% | GPT-5.6 Terra |
| DTBench | 93.9% | 93.3% | DeepSeek V4 Pro 0813 |
| LMCA | 45.5% | 54.9% | GPT-5.6 Terra |
| Surface Evolver Bench | Not measured | 83.8% | Only GPT-5.6 Terra measured |
| Furniture assembly | Not measured | 54.2% | Only GPT-5.6 Terra measured |
| LMArena Text | 1446 | 1448 | GPT-5.6 Terra |
| LMArena Text: instruction following | 1448 | 1456 | GPT-5.6 Terra |
| LMArena Text: creative writing | 1439 | 1413 | DeepSeek V4 Pro 0813 |
| LMArena Vision | Not measured | 1271 | Only GPT-5.6 Terra measured |
| LMArena Text: English | 1457 | 1449 | DeepSeek V4 Pro 0813 |
| LMArena Text: non-English | 1431 | 1441 | GPT-5.6 Terra |
| LMArena Text: Chinese | 1481 | 1514 | GPT-5.6 Terra |
| LMArena Text: German | Not measured | 1460 | Only GPT-5.6 Terra measured |
| LMArena Text: Japanese | Not measured | 1454 | Only GPT-5.6 Terra measured |
| LMArena Text: Korean | 1449 | 1427 | DeepSeek V4 Pro 0813 |
| LMArena Text: French | 1446 | 1466 | GPT-5.6 Terra |
| LMArena Text: Spanish | 1435 | 1451 | GPT-5.6 Terra |
| LMArena Text: Russian | 1448 | 1452 | GPT-5.6 Terra |
| LMArena Text: Polish | Not measured | 1446 | Only GPT-5.6 Terra measured |
| LMArena Text: multi-turn | 1454 | 1453 | DeepSeek V4 Pro 0813 |
| LMArena Text: longer queries | 1458 | 1454 | DeepSeek V4 Pro 0813 |
| LMArena Text: writing and language | 1443 | 1431 | DeepSeek V4 Pro 0813 |
| LMArena Text: entertainment and media | 1424 | 1418 | DeepSeek V4 Pro 0813 |
| LMArena Vision: English | Not measured | 1272 | Only GPT-5.6 Terra measured |
| LMArena Vision: Chinese | Not measured | 1337 | Only GPT-5.6 Terra measured |
| LMArena Vision: reading text | Not measured | 1278 | Only GPT-5.6 Terra measured |
| LMArena Vision: humor | Not measured | 1236 | Only GPT-5.6 Terra measured |
| LMArena Vision: creative writing | Not measured | 1248 | Only GPT-5.6 Terra measured |
| LMArena Document | Not measured | 1472 | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 10 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | GPT-5.6 Terra | 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 0813 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Pro 0813 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Pro 0813 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 0813 | OpenAI GPT-5.6 Terra |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 12, 2026 | July 9, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.66 | $2.00 |
| Output $/Mtok | $1.98 | $12.00 |
| Coding index | 54.6 | 69.0 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.10 | $0.42 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0813 better than GPT-5.6 Terra for coding?
Pick GPT-5.6 Terra for coding: it scores 69.0 on the Vibe Coding Index against 54.6 for DeepSeek V4 Pro 0813. Pick DeepSeek V4 Pro 0813 when output tokens cost 6.1x less, or when a solved task costs $0.10 against $0.42.
Which is cheaper, DeepSeek V4 Pro 0813 or GPT-5.6 Terra?
DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; GPT-5.6 Terra costs $2.00 and $12.00. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0813 costs $0.10 and GPT-5.6 Terra $0.42.
Which has the bigger context window, DeepSeek V4 Pro 0813 or GPT-5.6 Terra?
GPT-5.6 Terra, with 1.05M tokens against 1.05M.
How many benchmarks is the DeepSeek V4 Pro 0813 vs GPT-5.6 Terra comparison based on?
76 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Pro 0813's coding index rests on 1 independent benchmark and GPT-5.6 Terra's on 2. Where only one model has a result, the page says so instead of naming a winner.