DeepSeek V3.1 vs Mercury 2.5
DeepSeek V3.1 leads Mercury 2.5 on the Artificial Analysis Intelligence Index, 13.7 to 12.3, and Mercury 2.5 costs 10.0x less per output token ($0.15 vs $1.50 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
DeepSeek V3.1 or Mercury 2.5: which to pick
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; DeepSeek V3.1 has none, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for work measured by AA Intelligence Index, where it leads.
- Shared results
- 1
- DeepSeek V3.1 only
- 10
- Mercury 2.5 only
- 21
- 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
Only Mercury 2.5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V3.1
DeepSeek V3.1 leads, 13.7 against 12.3.
Same board, both measured
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
Mercury 2.5
Mercury 2.5 takes 260K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2.5
DeepSeek V3.1 $0.0013, Mercury 2.5 $0.0001 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, Mercury 2.5 1.6 from 2 benchmarks.
- Widest category
- Intelligence, 1.4 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: DeepSeek V3.1 13.7 against Mercury 2.5 12.3, 1.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against Mercury 2.5 $0.15 per million output tokens, 10.0x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, Mercury 2.5 260K.
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 | Mercury 2.5 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index9 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2.5 | Leader |
|---|---|---|---|
| Terminal-Bench 4 (Vals AI) | Not measured | 0.0% | Only Mercury 2.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 3.7% | Only Mercury 2.5 measured |
| Code migration (Vals AI) | Not measured | 4.5% | Only Mercury 2.5 measured |
| IOI (Vals AI) | Not measured | 2.4% | Only Mercury 2.5 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 34.5% | Only Mercury 2.5 measured |
| SkillsBench (Vals AI) | Not measured | 18.1% | Only Mercury 2.5 measured |
| CyberBench (Vals AI) | Not measured | 45.5% | Only Mercury 2.5 measured |
| SciCode | Not measured | 38.5% | Only Mercury 2.5 measured |
| ALE-Bench | Not measured | 301.6 | Only Mercury 2.5 measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.1 | Mercury 2.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 12.3 | DeepSeek V3.1 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2.5 | Leader |
|---|---|---|---|
| Finance Agent (Vals AI) | Not measured | 18.9% | Only Mercury 2.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 2.5% | Only Mercury 2.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 4.3% | Only Mercury 2.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 8.8% | Only Mercury 2.5 measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Mercury 2.5 measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2.5 | Leader |
|---|---|---|---|
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 11.8% | Only Mercury 2.5 measured |
| Kagi LLM Benchmark | 53.2% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2.5 | Leader |
|---|---|---|---|
| ProofBench | Not measured | 3.0% | Only Mercury 2.5 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2.5 | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | Not measured | 83.1% | Only Mercury 2.5 measured |
| MedCode (Vals AI) | Not measured | 31.3% | Only Mercury 2.5 measured |
| Neither model has a result on the other 16 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2.5 | 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 index5 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2.5 | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | Not measured | 45.3% | Only Mercury 2.5 measured |
| MedScribe (Vals AI) | Not measured | 55.1% | Only Mercury 2.5 measured |
| Long context reasoning (AA-LCR) | Not measured | 71.7% | Only Mercury 2.5 measured |
| DTBench | 82.7% | Not measured | Only DeepSeek V3.1 measured |
| LMCA | 24.3% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2.5 | 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
Mercury 2.5 costs less
Repository review
50K input + 3K output tokens
Mercury 2.5 costs less
Agent loop
200K input + 10K output tokens
Mercury 2.5 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 | Inception Mercury 2.5 |
|---|---|---|
| Maker | DeepSeek | Inception |
| Weights | Open weights | No public weights listed |
| Released | August 21, 2025 | September 8, 2026 |
| Context window | 164K | 260K |
| Input $/Mtok | $0.55 | $0.04 |
| Output $/Mtok | $1.50 | $0.15 |
| Coding index | Not benchmarked | 1.6 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 better than Mercury 2.5 for coding?
Only Mercury 2.5 has a coding score, 1.6 on the Vibe Coding Index; DeepSeek V3.1 has none, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for work measured by AA Intelligence Index, where it leads.
Which is cheaper, DeepSeek V3.1 or Mercury 2.5?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; Mercury 2.5 costs $0.04 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or Mercury 2.5?
Mercury 2.5, with 260K tokens against 164K.
How many benchmarks is the DeepSeek V3.1 vs Mercury 2.5 comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and Mercury 2.5's on 2. Where only one model has a result, the page says so instead of naming a winner.