DeepSeek V4 Flash 0423 vs GPT-5.6 Terra
GPT-5.6 Terra leads DeepSeek V4 Flash 0423 on the Artificial Analysis Intelligence Index, 42.1 to 24.4, and DeepSeek V4 Flash 0423 costs 42.9x less per output token ($0.28 vs $12.00 per million). Updated October 22, 2026; 49 benchmarks have a result for both.
DeepSeek V4 Flash 0423 or GPT-5.6 Terra: which to pick
Only GPT-5.6 Terra has a coding score, 63.9 on the Vibe Coding Index; DeepSeek V4 Flash 0423 has none, so the two cannot be ranked on coding.
Pick DeepSeek V4 Flash 0423 for output tokens 42.9x cheaper ($0.28 vs $12.00 per million) and work measured by LMArena WebDev or LMArena WebDev: React, where it leads.
- Shared results
- 49
- DeepSeek V4 Flash 0423 only
- 0
- GPT-5.6 Terra only
- 78
- 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
Only GPT-5.6 Terra has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 42.1 against 24.4.
Same board, both measured
Agentic work
AA Agentic Index
GPT-5.6 Terra
GPT-5.6 Terra leads, 43.2 against 26.3.
Same board, both measured
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
GPT-5.6 Terra
GPT-5.6 Terra leads, 1268 against 1207.
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 Flash 0423
DeepSeek V4 Flash 0423 $0.0003, 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 Flash 0423 not benchmarked, GPT-5.6 Terra 63.9 from 6 benchmarks.
- Widest category
- Intelligence, 17.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- ALE-Bench: DeepSeek V4 Flash 0423 678.2 against GPT-5.6 Terra 1951.4, 42.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash 0423 $0.28 against GPT-5.6 Terra $12.00 per million output tokens, 42.9x apart.
- Context window
- DeepSeek V4 Flash 0423 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 Flash 0423 | Bars | GPT-5.6 Terra | Ties |
|---|---|---|---|---|
| Coding | 3 | 5 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 4 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 3 | ||
| Web design | 1 | 7 | ||
| General | 0 | 19 | ||
| All shared boards | 6 | 43 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index35
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 86.2% | Only GPT-5.6 Terra measured |
| LMArena WebDev | 1582 | 1522 | DeepSeek V4 Flash 0423 |
| AA Coding Index | 52.0 | 76.7 | GPT-5.6 Terra |
| 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) | Not measured | 22.7% | Only GPT-5.6 Terra measured |
| Vibe Code Bench (Vals AI) | Not measured | 74.6% | Only GPT-5.6 Terra measured |
| Code migration (Vals AI) | Not measured | 47.8% | Only GPT-5.6 Terra measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only GPT-5.6 Terra measured |
| IOI (Vals AI) | Not measured | 87.6% | Only GPT-5.6 Terra measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 77.5% | Only GPT-5.6 Terra measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 14.8% | Only GPT-5.6 Terra measured |
| SkillsBench (Vals AI) | Not measured | 58.9% | Only GPT-5.6 Terra measured |
| CyberBench (Vals AI) | Not measured | 72.1% | Only GPT-5.6 Terra measured |
| SciCode | Not measured | 55.0% | Only GPT-5.6 Terra measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 62.9% | Only GPT-5.6 Terra measured |
| FrontierCode | Not measured | 41.3% | Only GPT-5.6 Terra measured |
| WeirdML | 45.6% | 78.3% | GPT-5.6 Terra |
| ALE-Bench | 678.2 | 1951.4 | GPT-5.6 Terra |
| LMArena Text: coding | 1457 | 1485 | GPT-5.6 Terra |
| LMArena Text: software and IT | 1453 | 1482 | GPT-5.6 Terra |
| LMArena WebDev: React | 1585 | 1513 | DeepSeek V4 Flash 0423 |
| LMArena WebDev: HTML | 1558 | 1526 | DeepSeek V4 Flash 0423 |
| LMArena WebDev: from an image | Not measured | 1541 | Only GPT-5.6 Terra measured |
| SciCode | Not measured | 10.8% | 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 Flash 0423 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Intelligence Index | 24.4 | 42.1 | GPT-5.6 Terra |
| Epoch Capabilities Index | 146.1 | 160.1 | GPT-5.6 Terra |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | 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 | 1111 | Only GPT-5.6 Terra measured |
| Design Arena: agents building Android apps | Not measured | 1062 | 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 | 1058 | Only GPT-5.6 Terra measured |
| Design Arena: PowerPoint slides | Not measured | 1105 | 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) | Not measured | 9.5% | Only GPT-5.6 Terra measured |
| Finance Agent (Vals AI) | Not measured | 54.4% | Only GPT-5.6 Terra measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.0% | Only GPT-5.6 Terra measured |
| Legal Research Bench (Vals AI) | Not measured | 41.4% | Only GPT-5.6 Terra measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 66.2% | Only GPT-5.6 Terra measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 10.0% | Only GPT-5.6 Terra measured |
| τ²-Bench Telecom | Not measured | 86.3% | Only GPT-5.6 Terra measured |
| APEX-Agents | Not measured | 58.2% | Only GPT-5.6 Terra measured |
| 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 20 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 90.9% | Only GPT-5.6 Terra measured |
| 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) | Not measured | 92.5% | Only GPT-5.6 Terra measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 42.9% | Only GPT-5.6 Terra measured |
| GPQA Diamond (Epoch AI run) | Not measured | 93.3% | Only GPT-5.6 Terra measured |
| ARC-AGI | Not measured | 96.5% | Only GPT-5.6 Terra measured |
| ARC-AGI-2 | Not measured | 83.9% | Only GPT-5.6 Terra measured |
| SimpleBench | 46.3% | 48.9% | GPT-5.6 Terra |
| Chess puzzles | Not measured | 47.0% | Only GPT-5.6 Terra measured |
| Mystery game puzzles | Not measured | 35.0% | Only GPT-5.6 Terra measured |
| LMArena Text: hard prompts | 1444 | 1469 | GPT-5.6 Terra |
| LMArena Text: expert | 1441 | 1496 | GPT-5.6 Terra |
| LMArena Text: hard prompts in English | 1449 | 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 |
| Kagi LLM Benchmark | 52.2% | 51.3% | DeepSeek V4 Flash 0423 |
| Neither model has a result on the other 5 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 99.7% | Only GPT-5.6 Terra measured |
| FrontierMath, tiers 1 to 3 | Not measured | 86.0% | Only GPT-5.6 Terra measured |
| FrontierMath, tier 4 | Not measured | 70.7% | Only GPT-5.6 Terra measured |
| ProofBench | Not measured | 74.0% | Only GPT-5.6 Terra measured |
| LMArena Text: math | 1426 | 1465 | GPT-5.6 Terra |
| LMArena Text: mathematical work | 1432 | 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 index13
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.7% | Only GPT-5.6 Terra measured |
| LegalBench (Vals AI) | Not measured | 85.1% | Only GPT-5.6 Terra measured |
| 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) | Not measured | 43.4% | Only GPT-5.6 Terra measured |
| SimpleQA Verified | Not measured | 43.2% | Only GPT-5.6 Terra measured |
| LMArena Text: science | 1446 | 1452 | GPT-5.6 Terra |
| LMArena Text: medicine and healthcare | 1448 | 1430 | DeepSeek V4 Flash 0423 |
| LMArena Text: legal and government | 1439 | 1461 | GPT-5.6 Terra |
| LMArena Text: business and finance | 1425 | 1448 | GPT-5.6 Terra |
| FACTS Benchmark Suite | Not measured | 52.0% | Only GPT-5.6 Terra measured |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| Design Arena: websites | 1207 | 1268 | GPT-5.6 Terra |
| Design Arena: UI components | 1162 | 1256 | GPT-5.6 Terra |
| Design Arena: game dev | 1196 | 1229 | GPT-5.6 Terra |
| Design Arena: data viz | 1127 | 1240 | GPT-5.6 Terra |
| Design Arena: 3D | 1188 | 1134 | DeepSeek V4 Flash 0423 |
| Design Arena: all code categories | 1205 | 1241 | GPT-5.6 Terra |
| Design Arena: SVG | 1155 | 1219 | GPT-5.6 Terra |
| Design Arena: ASCII art | 1109 | 1177 | GPT-5.6 Terra |
| LiveBench design | Not measured | 76.0% | Only GPT-5.6 Terra measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
| Benchmark | DeepSeek V4 Flash 0423 | GPT-5.6 Terra | Leader |
|---|---|---|---|
| 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) | Not measured | 82.9% | Only GPT-5.6 Terra measured |
| 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) | Not measured | 83.0% | Only GPT-5.6 Terra measured |
| DTBench | 86.4% | 93.3% | GPT-5.6 Terra |
| LMCA | 35.9% | 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 | 1432 | 1448 | GPT-5.6 Terra |
| LMArena Text: instruction following | 1421 | 1456 | GPT-5.6 Terra |
| LMArena Text: creative writing | 1403 | 1413 | GPT-5.6 Terra |
| LMArena Vision | Not measured | 1271 | Only GPT-5.6 Terra measured |
| LMArena Text: English | 1441 | 1449 | GPT-5.6 Terra |
| LMArena Text: non-English | 1419 | 1441 | GPT-5.6 Terra |
| LMArena Text: Chinese | 1463 | 1514 | GPT-5.6 Terra |
| LMArena Text: German | 1419 | 1460 | GPT-5.6 Terra |
| LMArena Text: Japanese | 1389 | 1454 | GPT-5.6 Terra |
| LMArena Text: Korean | 1383 | 1427 | GPT-5.6 Terra |
| LMArena Text: French | 1439 | 1466 | GPT-5.6 Terra |
| LMArena Text: Spanish | 1436 | 1451 | GPT-5.6 Terra |
| LMArena Text: Russian | 1427 | 1452 | GPT-5.6 Terra |
| LMArena Text: Polish | 1436 | 1446 | GPT-5.6 Terra |
| LMArena Text: multi-turn | 1449 | 1453 | GPT-5.6 Terra |
| LMArena Text: longer queries | 1434 | 1454 | GPT-5.6 Terra |
| LMArena Text: writing and language | 1410 | 1431 | GPT-5.6 Terra |
| LMArena Text: entertainment and media | 1400 | 1418 | GPT-5.6 Terra |
| 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 11 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash 0423 | 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 Flash 0423 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash 0423 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash 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 Flash 0423 | OpenAI GPT-5.6 Terra |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | April 24, 2026 | July 9, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.14 | $2.00 |
| Output $/Mtok | $0.28 | $12.00 |
| Coding index | Not benchmarked | 63.9 |
| Independent measurements behind it | 1 | 6 |
| Cost per solved task | Not computable | $0.42 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash 0423 better than GPT-5.6 Terra for coding?
Only GPT-5.6 Terra has a coding score, 63.9 on the Vibe Coding Index; DeepSeek V4 Flash 0423 has none, so the two cannot be ranked on coding. Pick DeepSeek V4 Flash 0423 for output tokens 42.9x cheaper ($0.28 vs $12.00 per million) and work measured by LMArena WebDev or LMArena WebDev: React, where it leads.
Which is cheaper, DeepSeek V4 Flash 0423 or GPT-5.6 Terra?
DeepSeek V4 Flash 0423 costs $0.14 per million input tokens and $0.28 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 Flash 0423 or GPT-5.6 Terra?
GPT-5.6 Terra, with 1.05M tokens against 1.05M.
How many benchmarks is the DeepSeek V4 Flash 0423 vs GPT-5.6 Terra comparison based on?
49 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Flash 0423's coding index rests on 1 independent benchmark and GPT-5.6 Terra's on 6. Where only one model has a result, the page says so instead of naming a winner.