DeepSeek V3.1 vs GPT-5 Pro
DeepSeek V3.1 costs 80.0x less per output token than GPT-5 Pro ($1.50 vs $120.00 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
DeepSeek V3.1 or GPT-5 Pro: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for output tokens 80.0x cheaper ($1.50 vs $120.00 per million) and work measured by Kagi LLM Benchmark, where it leads.
- Shared results
- 1
- DeepSeek V3.1 only
- 10
- GPT-5 Pro only
- 8
- Like-for-like categories
- 1 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only DeepSeek V3.1 has a AA Intelligence Index result, 13.7.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.1 has a Design Arena: websites result, 1123.
Directional only
Long documents
Listed context window
GPT-5 Pro
GPT-5 Pro takes 400K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.1
DeepSeek V3.1 $0.0013, GPT-5 Pro $0.075 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, GPT-5 Pro not benchmarked.
- Widest category
- Reasoning, 23.6 points apart on average over 1 shared benchmark.
- Widest single gap
- Kagi LLM Benchmark: DeepSeek V3.1 53.2% against GPT-5 Pro 76.8%, 23.6 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against GPT-5 Pro $120.00 per million output tokens, 80.0x apart.
- Context window
- DeepSeek V3.1 takes 164K 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 V3.1 | Bars | GPT-5 Pro | Ties |
|---|---|---|---|---|
| Reasoning | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index1 board, none shared
| Benchmark | DeepSeek V3.1 | GPT-5 Pro | Leader |
|---|---|---|---|
| WeirdML | Not measured | 60.4% | Only GPT-5 Pro measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | Not measured | Only DeepSeek V3.1 measured |
| Epoch Capabilities Index | Not measured | 150.6 | Only GPT-5 Pro measured |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5 Pro | Leader |
|---|---|---|---|
| Neither model has a result on 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 | GPT-5 Pro | Leader |
|---|---|---|---|
| ARC-AGI | Not measured | 70.2% | Only GPT-5 Pro measured |
| ARC-AGI-2 | Not measured | 18.3% | Only GPT-5 Pro measured |
| SimpleBench | Not measured | 61.6% | Only GPT-5 Pro measured |
| Chess puzzles | Not measured | 48.0% | Only GPT-5 Pro measured |
| Kagi LLM Benchmark | 53.2% | 76.8% | GPT-5 Pro |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5 Pro | Leader |
|---|---|---|---|
| FrontierMath, tiers 1 to 3 | Not measured | 55.8% | Only GPT-5 Pro measured |
| FrontierMath, tier 4 | Not measured | 19.5% | Only GPT-5 Pro measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5 Pro | Leader |
|---|---|---|---|
| Neither model has a result on 18 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1123 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: UI components | 1085 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: game dev | 1084 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: data viz | 1095 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: 3D | 1077 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: all code categories | 1112 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: SVG | 967 | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | 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 V3.1 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.1 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.1 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 V3.1 | OpenAI GPT-5 Pro |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 21, 2025 | October 6, 2025 |
| Context window | 164K | 400K |
| Input $/Mtok | $0.55 | $15.00 |
| Output $/Mtok | $1.50 | $120.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 better than GPT-5 Pro for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for output tokens 80.0x cheaper ($1.50 vs $120.00 per million) and work measured by Kagi LLM Benchmark, where it leads.
Which is cheaper, DeepSeek V3.1 or GPT-5 Pro?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; GPT-5 Pro costs $15.00 and $120.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or GPT-5 Pro?
GPT-5 Pro, with 400K tokens against 164K.
How many benchmarks is the DeepSeek V3.1 vs GPT-5 Pro comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.1's coding index rests on 0 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.