DeepSeek V4.1 Flash vs GPT-5.6 Terra
GPT-5.6 Terra leads DeepSeek V4.1 Flash on coding, 63.9 to 62.5 on the Vibe Coding Index, and DeepSeek V4.1 Flash costs 14.3x less per output token ($0.84 vs $12.00 per million). Updated October 22, 2026; 97 benchmarks have a result for both.
DeepSeek V4.1 Flash or GPT-5.6 Terra: which to pick
DeepSeek V4.1 Flash and GPT-5.6 Terra are level for coding, 62.5 against 63.9 on the Vibe Coding Index, so price and context decide.
Pick DeepSeek V4.1 Flash for output tokens 14.3x cheaper ($0.84 vs $12.00 per million) and work measured by LiveBench coding or LMArena WebDev, where it leads. Pick GPT-5.6 Terra for a larger context window (1.05M against 1.05M tokens) and work measured by Terminal-Bench 4 (Vals AI) or Code migration (Vals AI), where it leads.
- Shared results
- 97
- DeepSeek V4.1 Flash only
- 5
- GPT-5.6 Terra only
- 32
- 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
No call
Level: 62.5 against 63.9, inside 2 points.
5 and 6 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 42.1 against 24.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GPT-5.6 Terra has a AA Agentic Index result, 43.2.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-5.6 Terra has a GPQA Diamond result, 90.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5.6 Terra has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash leads, 1308 against 1268.
Same board, both measured
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 V4.1 Flash
DeepSeek V4.1 Flash $0.0006, GPT-5.6 Terra $0.0080 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.6 Terra has the number, $0.42.
Directional only
At a glance
- Coding index
- DeepSeek V4.1 Flash 62.5 from 5 benchmarks, GPT-5.6 Terra 63.9 from 6 benchmarks.
- Widest category
- Math, 14.5 points apart on average over 6 shared benchmarks.
- Widest single gap
- IOI (Vals AI): DeepSeek V4.1 Flash 40.3% against GPT-5.6 Terra 87.6%, 47.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash $0.84 against GPT-5.6 Terra $12.00 per million output tokens, 14.3x apart.
- Context window
- DeepSeek V4.1 Flash 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 V4.1 Flash | Bars | GPT-5.6 Terra | Ties |
|---|---|---|---|---|
| Coding | 12 | 6 | 1 tie | |
| Intelligence | 0 | 2 | ||
| Agentic work | 1 | 7 | 2 ties | |
| Reasoning | 6 | 6 | ||
| Math | 2 | 4 | ||
| Knowledge | 3 | 4 | ||
| Web design | 9 | 0 | ||
| General | 23 | 8 | 1 tie | |
| All shared boards | 56 | 37 | 4 ties |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index126
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench coding | 90.2% | 86.2% | DeepSeek V4.1 Flash |
| LMArena WebDev | 1618 | 1522 | DeepSeek V4.1 Flash |
| AA Coding Index | Not measured | 76.7 | Only GPT-5.6 Terra measured |
| LiveCodeBench (Vals AI) | Not measured | 85.9% | Only GPT-5.6 Terra measured |
| SWE-bench Verified (Vals AI) | Not measured | 95.4% | Only GPT-5.6 Terra measured |
| Terminal-Bench 4 (Vals AI) | 19.7% | 22.7% | GPT-5.6 Terra |
| Vibe Code Bench (Vals AI) | 84.7% | 74.6% | DeepSeek V4.1 Flash |
| Code migration (Vals AI) | 45.6% | 47.8% | GPT-5.6 Terra |
| ProgramBench (Vals AI) | 0.5% | 0.5% | Tie |
| IOI (Vals AI) | 40.3% | 87.6% | GPT-5.6 Terra |
| Terminal-Bench 2.1 (Vals AI) | 74.5% | 77.5% | GPT-5.6 Terra |
| SWE-bench Multimodal (Vals AI) | 46.5% | Not measured | Only DeepSeek V4.1 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | 16.4% | 14.8% | DeepSeek V4.1 Flash |
| SkillsBench (Vals AI) | 69.8% | 58.9% | DeepSeek V4.1 Flash |
| CyberBench (Vals AI) | 73.7% | 72.1% | DeepSeek V4.1 Flash |
| SciCode | 51.9% | 55.0% | GPT-5.6 Terra |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 62.9% | Only GPT-5.6 Terra measured |
| FrontierCode | 43.9% | 41.3% | DeepSeek V4.1 Flash |
| WeirdML | Not measured | 78.3% | Only GPT-5.6 Terra measured |
| ALE-Bench | 1092.3 | 1951.4 | GPT-5.6 Terra |
| LMArena Text: coding | 1502 | 1485 | DeepSeek V4.1 Flash |
| LMArena Text: software and IT | 1496 | 1482 | DeepSeek V4.1 Flash |
| LMArena WebDev: React | 1628 | 1513 | DeepSeek V4.1 Flash |
| LMArena WebDev: HTML | 1612 | 1526 | DeepSeek V4.1 Flash |
| LMArena WebDev: from an image | 1606 | 1541 | DeepSeek V4.1 Flash |
| SciCode | Not measured | 10.8% | Only GPT-5.6 Terra measured |
| SWE-Bench Pro V2 | Not measured | 92.4% | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.7 | 42.1 | GPT-5.6 Terra |
| Epoch Capabilities Index | 155.9 | 160.1 | GPT-5.6 Terra |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 43.2 | Only GPT-5.6 Terra measured |
| 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 | 1110 | Only GPT-5.6 Terra measured |
| Design Arena: agents building Android apps | Not measured | 1060 | Only GPT-5.6 Terra measured |
| Design Arena: agents building games | Not measured | 1083 | Only GPT-5.6 Terra measured |
| Design Arena: HTML slides | Not measured | 1047 | Only GPT-5.6 Terra measured |
| Design Arena: PowerPoint slides | Not measured | 1104 | Only GPT-5.6 Terra measured |
| LiveBench research | 86.8% | 60.4% | DeepSeek V4.1 Flash |
| LiveBench browser | 84.0% | 84.0% | Tie |
| LiveBench long agentic | 40.1% | 48.4% | GPT-5.6 Terra |
| SREBench (Vals AI) | 0.8% | 9.5% | GPT-5.6 Terra |
| Finance Agent (Vals AI) | 53.5% | 54.4% | GPT-5.6 Terra |
| Tax Agent Bench (Vals AI) | 26.6% | 30.0% | GPT-5.6 Terra |
| Legal Research Bench (Vals AI) | 41.4% | 41.4% | Tie |
| Excel Modeling Benchmark (Vals AI) | 57.2% | 66.2% | GPT-5.6 Terra |
| BioMysteryBench (Vals AI) | 67.8% | Not measured | Only DeepSeek V4.1 Flash measured |
| Terminal-Bench-Science (Vals AI) | 0.0% | 10.0% | GPT-5.6 Terra |
| Vals Science Index | 29.6% | Not measured | Only DeepSeek V4.1 Flash measured |
| τ²-Bench Telecom | Not measured | 86.3% | Only GPT-5.6 Terra measured |
| APEX-Agents | 39.5% | 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 19 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index66
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Terra | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 90.9% | Only GPT-5.6 Terra measured |
| LiveBench reasoning | 86.7% | 90.6% | GPT-5.6 Terra |
| MysteryMechanism (Vals AI) | 21.2% | Not measured | Only DeepSeek V4.1 Flash measured |
| MMMU Pro (Vals AI) | Not measured | 86.5% | Only GPT-5.6 Terra measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 92.5% | Only GPT-5.6 Terra measured |
| Humanity's Last Exam (Artificial Analysis run) | 39.2% | 42.9% | GPT-5.6 Terra |
| GPQA Diamond (Epoch AI run) | 89.8% | 93.3% | GPT-5.6 Terra |
| ARC-AGI | 94.5% | 96.5% | GPT-5.6 Terra |
| ARC-AGI-2 | 72.9% | 83.9% | GPT-5.6 Terra |
| SimpleBench | Not measured | 48.9% | Only GPT-5.6 Terra measured |
| Chess puzzles | 48.0% | 47.0% | DeepSeek V4.1 Flash |
| Mystery game puzzles | 43.0% | 35.0% | DeepSeek V4.1 Flash |
| LMArena Text: hard prompts | 1483 | 1469 | DeepSeek V4.1 Flash |
| LMArena Text: expert | 1501 | 1496 | DeepSeek V4.1 Flash |
| LMArena Text: hard prompts in English | 1488 | 1465 | DeepSeek V4.1 Flash |
| LMArena Vision: diagrams | 1297 | 1295 | DeepSeek V4.1 Flash |
| LMArena Vision: homework | 1307 | 1312 | GPT-5.6 Terra |
| Kagi LLM Benchmark | Not measured | 51.3% | 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 index24
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Terra | Leader |
|---|---|---|---|
| OTIS Mock AIME | 98.3% | 99.7% | GPT-5.6 Terra |
| FrontierMath, tiers 1 to 3 | 67.4% | 86.0% | GPT-5.6 Terra |
| FrontierMath, tier 4 | 26.8% | 70.7% | GPT-5.6 Terra |
| ProofBench | 54.0% | 74.0% | GPT-5.6 Terra |
| LMArena Text: math | 1478 | 1465 | DeepSeek V4.1 Flash |
| LMArena Text: mathematical work | 1485 | 1478 | DeepSeek V4.1 Flash |
| MathArena | 42.3% | Not measured | Only DeepSeek V4.1 Flash measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index34
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Terra | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.7% | Only GPT-5.6 Terra measured |
| LegalBench (Vals AI) | 83.3% | 85.1% | GPT-5.6 Terra |
| CorpFin (Vals AI) | Not measured | 65.3% | Only GPT-5.6 Terra measured |
| TaxEval (Vals AI) | Not measured | 76.2% | Only GPT-5.6 Terra measured |
| MortgageTax (Vals AI) | Not measured | 67.3% | Only GPT-5.6 Terra measured |
| MedCode (Vals AI) | 41.2% | 43.4% | GPT-5.6 Terra |
| SimpleQA Verified | 42.9% | 43.2% | GPT-5.6 Terra |
| LMArena Text: science | 1469 | 1452 | DeepSeek V4.1 Flash |
| LMArena Text: medicine and healthcare | 1484 | 1430 | DeepSeek V4.1 Flash |
| LMArena Text: legal and government | 1443 | 1461 | GPT-5.6 Terra |
| LMArena Text: business and finance | 1461 | 1448 | DeepSeek V4.1 Flash |
| FACTS Benchmark Suite | Not measured | 52.0% | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Terra | Leader |
|---|---|---|---|
| Design Arena: websites | 1308 | 1268 | DeepSeek V4.1 Flash |
| Design Arena: UI components | 1312 | 1256 | DeepSeek V4.1 Flash |
| Design Arena: game dev | 1330 | 1229 | DeepSeek V4.1 Flash |
| Design Arena: data viz | 1291 | 1240 | DeepSeek V4.1 Flash |
| Design Arena: 3D | 1355 | 1134 | DeepSeek V4.1 Flash |
| Design Arena: all code categories | 1319 | 1240 | DeepSeek V4.1 Flash |
| Design Arena: SVG | 1244 | 1219 | DeepSeek V4.1 Flash |
| Design Arena: ASCII art | 1265 | 1178 | DeepSeek V4.1 Flash |
| LiveBench design | 79.2% | 76.0% | DeepSeek V4.1 Flash |
GeneralLike-for-likePublic boards, shown as published, kept out of the index238
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench data analysis | 79.3% | 79.3% | Tie |
| Public Benefits Bench (Vals AI) | 64.3% | 62.4% | DeepSeek V4.1 Flash |
| SAGE (Vals AI) | 47.9% | 47.0% | DeepSeek V4.1 Flash |
| MedScribe (Vals AI) | 85.5% | 82.9% | DeepSeek V4.1 Flash |
| 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) | 84.0% | 83.0% | DeepSeek V4.1 Flash |
| DTBench | 89.9% | 93.3% | GPT-5.6 Terra |
| LMCA | 47.0% | 54.9% | GPT-5.6 Terra |
| Surface Evolver Bench | 46.3% | 83.8% | GPT-5.6 Terra |
| Furniture assembly | 34.2% | 54.2% | GPT-5.6 Terra |
| LMArena Text | 1464 | 1448 | DeepSeek V4.1 Flash |
| LMArena Text: instruction following | 1473 | 1456 | DeepSeek V4.1 Flash |
| LMArena Text: creative writing | 1437 | 1413 | DeepSeek V4.1 Flash |
| LMArena Vision | 1278 | 1271 | DeepSeek V4.1 Flash |
| LMArena Text: English | 1473 | 1449 | DeepSeek V4.1 Flash |
| LMArena Text: non-English | 1449 | 1441 | DeepSeek V4.1 Flash |
| LMArena Text: Chinese | 1501 | 1514 | GPT-5.6 Terra |
| LMArena Text: German | 1470 | 1460 | DeepSeek V4.1 Flash |
| LMArena Text: Japanese | 1432 | 1454 | GPT-5.6 Terra |
| LMArena Text: Korean | 1436 | 1427 | DeepSeek V4.1 Flash |
| LMArena Text: French | 1465 | 1466 | GPT-5.6 Terra |
| LMArena Text: Spanish | 1459 | 1451 | DeepSeek V4.1 Flash |
| LMArena Text: Russian | 1467 | 1452 | DeepSeek V4.1 Flash |
| LMArena Text: Polish | 1457 | 1446 | DeepSeek V4.1 Flash |
| LMArena Text: multi-turn | 1461 | 1453 | DeepSeek V4.1 Flash |
| LMArena Text: longer queries | 1473 | 1454 | DeepSeek V4.1 Flash |
| LMArena Text: writing and language | 1450 | 1431 | DeepSeek V4.1 Flash |
| LMArena Text: entertainment and media | 1445 | 1418 | DeepSeek V4.1 Flash |
| LMArena Vision: English | 1288 | 1272 | DeepSeek V4.1 Flash |
| LMArena Vision: Chinese | 1327 | 1337 | GPT-5.6 Terra |
| LMArena Vision: reading text | 1282 | 1278 | DeepSeek V4.1 Flash |
| LMArena Vision: humor | 1260 | 1236 | DeepSeek V4.1 Flash |
| LMArena Vision: creative writing | 1275 | 1248 | DeepSeek V4.1 Flash |
| LMArena Document | Not measured | 1472 | Only GPT-5.6 Terra measured |
| EQ-Bench 4 | Not measured | 1234 | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash | 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 V4.1 Flash costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 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 V4.1 Flash | OpenAI GPT-5.6 Terra |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | September 10, 2026 | July 9, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.21 | $2.00 |
| Output $/Mtok | $0.84 | $12.00 |
| Coding index | 62.5 | 63.9 |
| Independent measurements behind it | 5 | 6 |
| Cost per solved task | Not computable | $0.42 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash better than GPT-5.6 Terra for coding?
DeepSeek V4.1 Flash and GPT-5.6 Terra are level for coding, 62.5 against 63.9 on the Vibe Coding Index, so price and context decide. Pick DeepSeek V4.1 Flash for output tokens 14.3x cheaper ($0.84 vs $12.00 per million) and work measured by LiveBench coding or LMArena WebDev, where it leads. Pick GPT-5.6 Terra for a larger context window (1.05M against 1.05M tokens) and work measured by Terminal-Bench 4 (Vals AI) or Code migration (Vals AI), where it leads.
Which is cheaper, DeepSeek V4.1 Flash or GPT-5.6 Terra?
DeepSeek V4.1 Flash costs $0.21 per million input tokens and $0.84 per million output tokens; GPT-5.6 Terra costs $2.00 and $12.00. Cost per solved task is known for GPT-5.6 Terra only.
Which has the bigger context window, DeepSeek V4.1 Flash or GPT-5.6 Terra?
GPT-5.6 Terra, with 1.05M tokens against 1.05M.
How many benchmarks is the DeepSeek V4.1 Flash vs GPT-5.6 Terra comparison based on?
97 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash's coding index rests on 5 independent benchmarks and GPT-5.6 Terra's on 6. Where only one model has a result, the page says so instead of naming a winner.