DeepSeek V3.1 vs Granite 4.0 Micro
DeepSeek V3.1 leads Granite 4.0 Micro on the Artificial Analysis Intelligence Index, 13.7 to 5.1, and Granite 4.0 Micro costs 13.4x less per output token ($0.112 vs $1.50 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
DeepSeek V3.1 or Granite 4.0 Micro: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for a larger context window (164K against 131K tokens).
- Shared results
- 1
- DeepSeek V3.1 only
- 10
- Granite 4.0 Micro only
- 12
- 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
DeepSeek V3.1
DeepSeek V3.1 leads, 13.7 against 5.1.
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
DeepSeek V3.1
DeepSeek V3.1 takes 164K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Granite 4.0 Micro
DeepSeek V3.1 $0.0013, Granite 4.0 Micro $0.0001 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, Granite 4.0 Micro not benchmarked.
- Widest category
- Intelligence, 8.6 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: DeepSeek V3.1 13.7 against Granite 4.0 Micro 5.1, 8.6 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against Granite 4.0 Micro $0.112 per million output tokens, 13.4x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, Granite 4.0 Micro 131K.
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 | Granite 4.0 Micro | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards, none shared
| Benchmark | DeepSeek V3.1 | Granite 4.0 Micro | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 18.0% | Only Granite 4.0 Micro measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 1.5% | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.1 | Granite 4.0 Micro | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 5.1 | 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 index1 board, none shared
| Benchmark | DeepSeek V3.1 | Granite 4.0 Micro | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 12.6% | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.1 | Granite 4.0 Micro | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 33.6% | Only Granite 4.0 Micro measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 5.0% | Only Granite 4.0 Micro measured |
| GPQA Diamond (Epoch AI run) | Not measured | 28.3% | Only Granite 4.0 Micro measured |
| Kagi LLM Benchmark | 53.2% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3.1 | Granite 4.0 Micro | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 6.0 | Only Granite 4.0 Micro measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 6.0% | Only Granite 4.0 Micro measured |
| OTIS Mock AIME | Not measured | 2.8% | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3.1 | Granite 4.0 Micro | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 44.7% | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 19 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.1 | Granite 4.0 Micro | Leader |
|---|---|---|---|
| Design Arena: websites | 1122 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: UI components | 1083 | 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 index4 boards, none shared
| Benchmark | DeepSeek V3.1 | Granite 4.0 Micro | Leader |
|---|---|---|---|
| IFBench | Not measured | 24.8% | Only Granite 4.0 Micro measured |
| Long context reasoning (AA-LCR) | Not measured | 7.0% | Only Granite 4.0 Micro 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 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Granite 4.0 Micro | 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
Granite 4.0 Micro costs less
Repository review
50K input + 3K output tokens
Granite 4.0 Micro costs less
Agent loop
200K input + 10K output tokens
Granite 4.0 Micro 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 | IBM Granite 4.0 Micro |
|---|---|---|
| Maker | DeepSeek | IBM Granite |
| Weights | Open weights | Open weights |
| Released | August 21, 2025 | October 20, 2025 |
| Context window | 164K | 131K |
| Input $/Mtok | $0.55 | $0.017 |
| Output $/Mtok | $1.50 | $0.112 |
| 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 Granite 4.0 Micro for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for a larger context window (164K against 131K tokens).
Which is cheaper, DeepSeek V3.1 or Granite 4.0 Micro?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; Granite 4.0 Micro costs $0.017 and $0.112. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or Granite 4.0 Micro?
DeepSeek V3.1, with 164K tokens against 131K.
How many benchmarks is the DeepSeek V3.1 vs Granite 4.0 Micro 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 Granite 4.0 Micro's on 0. Where only one model has a result, the page says so instead of naming a winner.