DeepSeek V3.1 vs GPT-5.2 Pro
DeepSeek V3.1 costs 112.0x less per output token than GPT-5.2 Pro ($1.50 vs $168.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.50
- Coding
- No data
- Overall
- No data
- Output $/M
- $168.00
DeepSeek V3.1 or GPT-5.2 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 112.0x cheaper ($1.50 vs $168.00 per million).
No benchmark has tested both yet. DeepSeek V3.1 has results on AA Intelligence Index, Kagi LLM Benchmark and Design Arena: websites; GPT-5.2 Pro on Epoch Capabilities Index, ARC-AGI and ARC-AGI-2. Compare them on price and context instead.
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, 1122.
Directional only
Long documents
Listed context window
GPT-5.2 Pro
GPT-5.2 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-5.2 Pro $0.105 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, GPT-5.2 Pro not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- DeepSeek V3.1 $1.50 against GPT-5.2 Pro $168.00 per million output tokens, 112.0x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, GPT-5.2 Pro 400K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | DeepSeek V3.1 | GPT-5.2 Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | Not measured | Only DeepSeek V3.1 measured |
| Epoch Capabilities Index | Not measured | 156.3 | Only GPT-5.2 Pro measured |
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | DeepSeek V3.1 | GPT-5.2 Pro | Leader |
|---|---|---|---|
| ARC-AGI | Not measured | 90.5% | Only GPT-5.2 Pro measured |
| ARC-AGI-2 | Not measured | 54.2% | Only GPT-5.2 Pro measured |
| SimpleBench | Not measured | 57.4% | Only GPT-5.2 Pro measured |
| Chess puzzles | Not measured | 60.0% | Only GPT-5.2 Pro measured |
| Kagi LLM Benchmark | 53.2% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | DeepSeek V3.1 | GPT-5.2 Pro | Leader |
|---|---|---|---|
| FrontierMath, tiers 1 to 3 | Not measured | 74.0% | Only GPT-5.2 Pro measured |
| FrontierMath, tier 4 | Not measured | 46.0% | Only GPT-5.2 Pro measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | DeepSeek V3.1 | GPT-5.2 Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1122 | 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 | 1096 | 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 measured
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-5.2 Pro |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 21, 2025 | December 10, 2025 |
| Context window | 164K | 400K |
| Input $/Mtok | $0.55 | $21.00 |
| Output $/Mtok | $1.50 | $168.00 |
| 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-5.2 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 112.0x cheaper ($1.50 vs $168.00 per million).
Which is cheaper, DeepSeek V3.1 or GPT-5.2 Pro?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; GPT-5.2 Pro costs $21.00 and $168.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or GPT-5.2 Pro?
GPT-5.2 Pro, with 400K tokens against 164K.
Has any benchmark tested both DeepSeek V3.1 and GPT-5.2 Pro?
No benchmark has tested both yet. DeepSeek V3.1 has results on AA Intelligence Index, Kagi LLM Benchmark and Design Arena: websites; GPT-5.2 Pro on Epoch Capabilities Index, ARC-AGI and ARC-AGI-2. Compare them on price and context instead. Data as of October 22, 2026.