DeepSeek V4 Flash 0731 vs GPT-5 Pro
DeepSeek V4 Flash 0731 costs 428.6x less per output token than GPT-5 Pro ($0.28 vs $120.00 per million). Updated October 21, 2026; 8 benchmarks have a result for both.
DeepSeek V4 Flash 0731 or GPT-5 Pro: which to pick
Only DeepSeek V4 Flash 0731 has a coding score, 51.8 on the Vibe Coding Index; GPT-5 Pro has none, so the two cannot be ranked on coding.
Pick GPT-5 Pro for work measured by SimpleBench or Chess puzzles, where it leads.
- Shared results
- 8
- DeepSeek V4 Flash 0731 only
- 47
- GPT-5 Pro only
- 1
- Like-for-like categories
- 4 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 DeepSeek V4 Flash 0731 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only DeepSeek V4 Flash 0731 has a AA Intelligence Index result, 34.3.
Directional only
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Flash 0731 has a AA Agentic Index result, 41.0.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Flash 0731 has a GPQA Diamond result, 89.9%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Flash 0731 has a MMLU Pro result, 86.2%.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V4 Flash 0731 has a Design Arena: websites result, 1238.
Directional only
Long documents
Listed context window
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Flash 0731
DeepSeek V4 Flash 0731 $0.0003, GPT-5 Pro $0.075 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V4 Flash 0731 has the number, $0.01.
Directional only
At a glance
- Coding index
- DeepSeek V4 Flash 0731 51.8 from 4 benchmarks, GPT-5 Pro not benchmarked.
- Widest category
- Reasoning, 19.6 points apart on average over 4 shared benchmarks.
- Widest single gap
- ARC-AGI-2: DeepSeek V4 Flash 0731 61.4% against GPT-5 Pro 18.3%, 43.1 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Flash 0731 $0.28 against GPT-5 Pro $120.00 per million output tokens, 428.6x apart.
- Context window
- DeepSeek V4 Flash 0731 takes 1.05M tokens, GPT-5 Pro 400K.
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 0731 | Bars | GPT-5 Pro | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 2 | 2 | ||
| Math | 2 | 0 | ||
| All shared boards | 6 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | DeepSeek V4 Flash 0731 | GPT-5 Pro | Leader |
|---|---|---|---|
| AA Coding Index | 69.1 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| LiveCodeBench (Vals AI) | 87.3% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| SWE-bench Verified (Vals AI) | 88.8% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Terminal-Bench 4 (Vals AI) | 18.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Vibe Code Bench (Vals AI) | 74.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Code migration (Vals AI) | 38.6% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| IOI (Vals AI) | 32.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Terminal-Bench 2.1 (Vals AI) | 67.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| SkillsBench (Vals AI) | 50.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| CyberBench (Vals AI) | 72.1% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| SciCode | 50.3% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| FrontierCode | 18.8% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| WeirdML | 63.0% | 60.4% | DeepSeek V4 Flash 0731 |
| ALE-Bench | 1306.1 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Flash 0731 | GPT-5 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 34.3 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Epoch Capabilities Index | 154.8 | 150.6 | DeepSeek V4 Flash 0731 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V4 Flash 0731 | GPT-5 Pro | Leader |
|---|---|---|---|
| AA Agentic Index | 41.0 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Finance Agent (Vals AI) | 49.5% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Tax Agent Bench (Vals AI) | 28.1% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Legal Research Bench (Vals AI) | 30.3% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Excel Modeling Benchmark (Vals AI) | 57.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| BioMysteryBench (Vals AI) | 64.4% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | DeepSeek V4 Flash 0731 | GPT-5 Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 89.9% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| GPQA Diamond (Artificial Analysis run) | 90.8% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Humanity's Last Exam (Artificial Analysis run) | 38.6% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| GPQA Diamond (Epoch AI run) | 91.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| ARC-AGI | 89.0% | 70.2% | DeepSeek V4 Flash 0731 |
| ARC-AGI-2 | 61.4% | 18.3% | DeepSeek V4 Flash 0731 |
| SimpleBench | 61.1% | 61.6% | GPT-5 Pro |
| Chess puzzles | 32.0% | 48.0% | GPT-5 Pro |
| Mystery game puzzles | 34.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Kagi LLM Benchmark | Not measured | 76.8% | Only GPT-5 Pro measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Flash 0731 | GPT-5 Pro | Leader |
|---|---|---|---|
| OTIS Mock AIME | 94.4% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| FrontierMath, tiers 1 to 3 | 57.5% | 55.8% | DeepSeek V4 Flash 0731 |
| FrontierMath, tier 4 | 24.4% | 19.5% | DeepSeek V4 Flash 0731 |
| ProofBench | 56.0% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V4 Flash 0731 | GPT-5 Pro | Leader |
|---|---|---|---|
| MMLU Pro | 86.2% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| LegalBench (Vals AI) | 77.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| CorpFin (Vals AI) | 61.9% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| TaxEval (Vals AI) | 70.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| MedCode (Vals AI) | 41.4% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| SimpleQA Verified | 33.6% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4 Flash 0731 | GPT-5 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1238 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: UI components | 1235 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: game dev | 1208 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: data viz | 1180 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: 3D | 1209 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: all code categories | 1230 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: SVG | 1185 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Design Arena: ASCII art | 1090 | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V4 Flash 0731 | GPT-5 Pro | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 80.4% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Long context reasoning (AA-LCR) | 79.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| DTBench | 90.9% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| LMCA | 41.7% | Not measured | Only DeepSeek V4 Flash 0731 measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Flash 0731 | GPT-5 Pro | 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 0731 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4 Flash 0731 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4 Flash 0731 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 0731 | OpenAI GPT-5 Pro |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | July 31, 2026 | October 6, 2025 |
| Context window | 1.05M | 400K |
| Input $/Mtok | $0.14 | $15.00 |
| Output $/Mtok | $0.28 | $120.00 |
| Coding index | 51.8 | Not benchmarked |
| Independent measurements behind it | 4 | 0 |
| Cost per solved task | $0.01 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Flash 0731 better than GPT-5 Pro for coding?
Only DeepSeek V4 Flash 0731 has a coding score, 51.8 on the Vibe Coding Index; GPT-5 Pro has none, so the two cannot be ranked on coding. Pick GPT-5 Pro for work measured by SimpleBench or Chess puzzles, where it leads.
Which is cheaper, DeepSeek V4 Flash 0731 or GPT-5 Pro?
DeepSeek V4 Flash 0731 costs $0.14 per million input tokens and $0.28 per million output tokens; GPT-5 Pro costs $15.00 and $120.00. Cost per solved task is known for DeepSeek V4 Flash 0731 only.
Which has the bigger context window, DeepSeek V4 Flash 0731 or GPT-5 Pro?
DeepSeek V4 Flash 0731, with 1.05M tokens against 400K.
How many benchmarks is the DeepSeek V4 Flash 0731 vs GPT-5 Pro comparison based on?
8 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Flash 0731's coding index rests on 4 independent benchmarks and GPT-5 Pro's on 0. Where only one model has a result, the page says so instead of naming a winner.