Granite 4.0 Micro vs LongCat 2.0
LongCat 2.0 leads Granite 4.0 Micro on the Artificial Analysis Intelligence Index, 19.1 to 5.1, and Granite 4.0 Micro costs 10.7x less per output token ($0.112 vs $1.20 per million). Updated October 23, 2026; 4 benchmarks have a result for both.
Granite 4.0 Micro or LongCat 2.0: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Granite 4.0 Micro for output tokens 10.7x cheaper ($0.112 vs $1.20 per million) and work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
- Shared results
- 4
- Granite 4.0 Micro only
- 9
- LongCat 2.0 only
- 4
- Like-for-like categories
- 3 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
LongCat 2.0
LongCat 2.0 leads, 19.1 against 5.1.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only LongCat 2.0 has a AA Agentic Index result, 14.0.
Directional only
Long documents
Listed context window
LongCat 2.0
LongCat 2.0 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Granite 4.0 Micro
Granite 4.0 Micro $0.0001, LongCat 2.0 $0.0009 per turn.
List prices
At a glance
- Coding index
- Granite 4.0 Micro not benchmarked, LongCat 2.0 not benchmarked.
- Widest category
- General, 58.0 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Granite 4.0 Micro 7.0% against LongCat 2.0 65.0%, 58.0 points apart on a 0 to 100 scale.
- Output price
- Granite 4.0 Micro $0.112 against LongCat 2.0 $1.20 per million output tokens, 10.7x apart.
- Context window
- Granite 4.0 Micro takes 131K tokens, LongCat 2.0 1.05M.
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 | Granite 4.0 Micro | Bars | LongCat 2.0 | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 4 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | Granite 4.0 Micro | LongCat 2.0 | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 45.3 | Only LongCat 2.0 measured |
| LiveCodeBench (Artificial Analysis run) | 18.0% | Not measured | Only Granite 4.0 Micro measured |
| SciCode | Not measured | 36.3% | Only LongCat 2.0 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | Not measured | Only Granite 4.0 Micro measured |
| ALE-Bench | Not measured | 793.5 | Only LongCat 2.0 measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Granite 4.0 Micro | LongCat 2.0 | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.1 | 19.1 | LongCat 2.0 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Granite 4.0 Micro | LongCat 2.0 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 14.0 | Only LongCat 2.0 measured |
| τ²-Bench Telecom | 12.6% | Not measured | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Granite 4.0 Micro | LongCat 2.0 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 33.6% | 78.0% | LongCat 2.0 |
| Humanity's Last Exam (Artificial Analysis run) | 5.0% | 33.7% | LongCat 2.0 |
| GPQA Diamond (Epoch AI run) | 28.3% | Not measured | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Granite 4.0 Micro | LongCat 2.0 | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | Not measured | Only Granite 4.0 Micro measured |
| AIME 2025 (Artificial Analysis run) | 6.0% | Not measured | Only Granite 4.0 Micro measured |
| OTIS Mock AIME | 2.8% | Not measured | 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 | Granite 4.0 Micro | LongCat 2.0 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 44.7% | Not measured | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 19 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Granite 4.0 Micro | LongCat 2.0 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Granite 4.0 Micro | LongCat 2.0 | Leader |
|---|---|---|---|
| IFBench | 24.8% | Not measured | Only Granite 4.0 Micro measured |
| Long context reasoning (AA-LCR) | 7.0% | 65.0% | LongCat 2.0 |
| Neither model has a result on the other 46 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Granite 4.0 Micro | LongCat 2.0 | 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 | IBM Granite 4.0 Micro | Meituan LongCat 2.0 |
|---|---|---|
| Maker | IBM Granite | Meituan |
| Weights | Open weights | Open weights |
| Released | October 20, 2025 | July 20, 2026 |
| Context window | 131K | 1.05M |
| Input $/Mtok | $0.017 | $0.30 |
| Output $/Mtok | $0.112 | $1.20 |
| 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 Granite 4.0 Micro better than LongCat 2.0 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Granite 4.0 Micro for output tokens 10.7x cheaper ($0.112 vs $1.20 per million) and work measured by AA Intelligence Index or GPQA Diamond (Artificial Analysis run), where it leads.
Which is cheaper, Granite 4.0 Micro or LongCat 2.0?
Granite 4.0 Micro costs $0.017 per million input tokens and $0.112 per million output tokens; LongCat 2.0 costs $0.30 and $1.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Granite 4.0 Micro or LongCat 2.0?
LongCat 2.0, with 1.05M tokens against 131K.
How many benchmarks is the Granite 4.0 Micro vs LongCat 2.0 comparison based on?
4 benchmarks have a result for both models as of October 23, 2026. Granite 4.0 Micro's coding index rests on 0 independent benchmarks and LongCat 2.0's on 0. Where only one model has a result, the page says so instead of naming a winner.