DeepSeek V4 Pro 0423 vs GPT-6.1 Terra
GPT-6.1 Terra leads DeepSeek V4 Pro 0423 on coding, 77.4 to 38.7 on the Vibe Coding Index, and DeepSeek V4 Pro 0423 costs 1.6x less per output token ($3.12 vs $5.00 per million). Updated October 29, 2026; 19 benchmarks have a result for both.
DeepSeek V4 Pro 0423 or GPT-6.1 Terra: which to pick
Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 4 benchmarks against 5.
Pick DeepSeek V4 Pro 0423 for output tokens 1.6x cheaper ($3.12 vs $5.00 per million) and work measured by Tax Agent Bench (Vals AI), where it leads.
- Shared results
- 19
- DeepSeek V4 Pro 0423 only
- 69
- GPT-6.1 Terra only
- 17
- Like-for-like categories
- 6 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-6.1 Terra
GPT-6.1 Terra scores 77.4 against 38.7.
5 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Only GPT-6.1 Terra has a AA Intelligence Index result, 51.0.
Directional only
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0423 has a AA Agentic Index result, 26.3.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Pro 0423 has a GPQA Diamond result, 89.4%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Pro 0423 has a MMLU Pro result, 87.3%.
Directional only
Web design
Design Arena: websites
GPT-6.1 Terra
GPT-6.1 Terra leads, 1283 against 1233.
Same board, both measured
General
Vals Index
No call
Only GPT-6.1 Terra has a Vals Index result, 60.5%.
Directional only
Long documents
Listed context window
GPT-6.1 Terra
GPT-6.1 Terra 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 $0.0030, GPT-6.1 Terra $0.0035 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V4 Pro 0423 has the number, $0.57.
Directional only
At a glance
- Coding index
- DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, GPT-6.1 Terra 77.4 from 4 benchmarks.
- Widest category
- Math, 46.4 points apart on average over 3 shared benchmarks.
- Widest single gap
- FrontierMath, tier 4: DeepSeek V4 Pro 0423 2.4% against GPT-6.1 Terra 92.7%, 90.2 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $3.12 against GPT-6.1 Terra $5.00 per million output tokens, 1.6x apart.
- Context window
- DeepSeek V4 Pro 0423 takes 1.05M tokens, GPT-6.1 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 0423 | Bars | GPT-6.1 Terra | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Agentic work | 1 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 1 | ||
| Web design | 0 | 4 | ||
| All shared boards | 1 | 18 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | DeepSeek V4 Pro 0423 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 94.3% | Only GPT-6.1 Terra measured |
| LMArena WebDev | 1447 | 1673 | GPT-6.1 Terra |
| AA Coding Index | 59.4 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveCodeBench (Vals AI) | 87.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SWE-bench Verified (Vals AI) | 77.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 4 (Vals AI) | 11.1% | 49.5% | GPT-6.1 Terra |
| Vibe Code Bench (Vals AI) | 49.9% | 84.0% | GPT-6.1 Terra |
| Code migration (Vals AI) | 26.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SkillsBench (Vals AI) | 51.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SciCode | Not measured | 55.1% | Only GPT-6.1 Terra measured |
| FrontierCode | 17.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| WeirdML | 48.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ALE-Bench | 1006.1 | 2608.8 | GPT-6.1 Terra |
| LMArena Text: coding | 1469 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: software and IT | 1468 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena WebDev: React | 1440 | 1657 | GPT-6.1 Terra |
| LMArena WebDev: HTML | 1435 | 1719 | GPT-6.1 Terra |
| SWE Atlas: Codebase QnA | 27.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SWE Atlas: Test Writing | 27.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 51.0 | Only GPT-6.1 Terra measured |
| Epoch Capabilities Index | 149.1 | Not measured | Only DeepSeek V4 Pro 0423 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | DeepSeek V4 Pro 0423 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building web apps | 1000 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building full-stack apps | 948 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building Godot games | 1059 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveBench research | Not measured | 73.9% | Only GPT-6.1 Terra measured |
| LiveBench browser | Not measured | 93.1% | Only GPT-6.1 Terra measured |
| LiveBench long agentic | Not measured | 48.6% | Only GPT-6.1 Terra measured |
| Finance Agent (Vals AI) | 44.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Tax Agent Bench (Vals AI) | 25.2% | 15.2% | DeepSeek V4 Pro 0423 |
| Legal Research Bench (Vals AI) | 23.1% | 26.0% | GPT-6.1 Terra |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 1.7% | Only GPT-6.1 Terra measured |
| Excel Modeling Benchmark (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| APEX-Agents | Not measured | 68.3% | Only GPT-6.1 Terra measured |
| Vending-Bench 2 | $3,285 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V4 Pro 0423 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveBench reasoning | Not measured | 88.7% | Only GPT-6.1 Terra measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 46.2% | Only GPT-6.1 Terra measured |
| GPQA Diamond (Epoch AI run) | 90.9% | 94.3% | GPT-6.1 Terra |
| Chess puzzles | 19.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Mystery game puzzles | 17.0% | 74.0% | GPT-6.1 Terra |
| EBR-bench | Not measured | 78.1% | Only GPT-6.1 Terra measured |
| LMArena Text: hard prompts | 1461 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: expert | 1463 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: hard prompts in English | 1466 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Kagi LLM Benchmark | 53.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Humanity's Last Exam (Scale run) | 13.4% | 35.7% | GPT-6.1 Terra |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | DeepSeek V4 Pro 0423 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| OTIS Mock AIME | 96.7% | 100.0% | GPT-6.1 Terra |
| FrontierMath, tiers 1 to 3 | 45.3% | 90.9% | GPT-6.1 Terra |
| FrontierMath, tier 4 | 2.4% | 92.7% | GPT-6.1 Terra |
| ProofBench | 16.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: math | 1442 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: mathematical work | 1455 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MathArena | 26.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Pro 0423 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LegalBench (Vals AI) | 80.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CaseLaw (Vals AI) | 59.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CorpFin (Vals AI) | 61.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| TaxEval (Vals AI) | 72.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MedCode (Vals AI) | 40.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SimpleQA Verified | 47.0% | 57.3% | GPT-6.1 Terra |
| LMArena Text: science | 1470 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: medicine and healthcare | 1466 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: legal and government | 1464 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: business and finance | 1443 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 9 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V4 Pro 0423 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| Design Arena: websites | 1233 | 1283 | GPT-6.1 Terra |
| Design Arena: UI components | 1216 | 1277 | GPT-6.1 Terra |
| Design Arena: game dev | 1228 | 1348 | GPT-6.1 Terra |
| Design Arena: data viz | 1204 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: 3D | 1250 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: all code categories | 1239 | 1295 | GPT-6.1 Terra |
| Design Arena: SVG | 1137 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: ASCII art | 1146 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveBench design | Not measured | 82.3% | Only GPT-6.1 Terra measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index29 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | GPT-6.1 Terra | Leader |
|---|---|---|---|
| Vals Index | Not measured | 60.5% | Only GPT-6.1 Terra measured |
| LiveBench data analysis | Not measured | 82.3% | Only GPT-6.1 Terra measured |
| Public Benefits Bench (Vals AI) | 62.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MedScribe (Vals AI) | 75.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Long context reasoning (AA-LCR) | Not measured | 82.7% | Only GPT-6.1 Terra measured |
| DTBench | 90.7% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMCA | 41.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Surface Evolver Bench | 40.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Furniture assembly | Not measured | 79.2% | Only GPT-6.1 Terra measured |
| CL-bench Life | 13.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text | 1451 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: instruction following | 1445 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: creative writing | 1446 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: English | 1459 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: non-English | 1439 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Chinese | 1482 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: German | 1457 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Japanese | 1445 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Korean | 1409 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: French | 1473 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Spanish | 1456 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Russian | 1453 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Polish | 1461 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: multi-turn | 1467 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: longer queries | 1457 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: writing and language | 1447 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: entertainment and media | 1431 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MultiChallenge | Not measured | 74.2% | Only GPT-6.1 Terra measured |
| EQ-Bench 4 | 1156 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 19 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | GPT-6.1 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 0423 costs less
Repository review
50K input + 3K output tokens
GPT-6.1 Terra costs less
Agent loop
200K input + 10K output tokens
GPT-6.1 Terra 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-6.1 Terra |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | April 24, 2026 | October 26, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.416 | $1.00 |
| Output $/Mtok | $3.12 | $5.00 |
| Coding index | 38.7 | 77.4 |
| Independent measurements behind it | 5 | 4 |
| Cost per solved task | $0.57 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than GPT-6.1 Terra for coding?
Pick GPT-6.1 Terra for coding: it scores 77.4 on the Vibe Coding Index against 38.7 for DeepSeek V4 Pro 0423, though on 4 benchmarks against 5. Pick DeepSeek V4 Pro 0423 for output tokens 1.6x cheaper ($3.12 vs $5.00 per million) and work measured by Tax Agent Bench (Vals AI), where it leads.
Which is cheaper, DeepSeek V4 Pro 0423 or GPT-6.1 Terra?
DeepSeek V4 Pro 0423 costs $1.416 per million input tokens and $3.12 per million output tokens; GPT-6.1 Terra costs $1.00 and $5.00. Cost per solved task is known for DeepSeek V4 Pro 0423 only.
Which has the bigger context window, DeepSeek V4 Pro 0423 or GPT-6.1 Terra?
GPT-6.1 Terra, with 1.05M tokens against 1.05M.
How many benchmarks is the DeepSeek V4 Pro 0423 vs GPT-6.1 Terra comparison based on?
19 benchmarks have a result for both models as of October 29, 2026. DeepSeek V4 Pro 0423's coding index rests on 5 independent benchmarks and GPT-6.1 Terra's on 4. Where only one model has a result, the page says so instead of naming a winner.