Granite 4.0 Micro vs Qwen2.5 7B Instruct
Granite 4.0 Micro costs 1.8x less per output token than Qwen2.5 7B Instruct ($0.112 vs $0.20 per million). Updated October 22, 2026; 2 benchmarks have a result for both.
Granite 4.0 Micro or Qwen2.5 7B Instruct: 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 1.8x cheaper ($0.112 vs $0.20 per million) and a larger context window (131K against 33K tokens).
- Shared results
- 2
- Granite 4.0 Micro only
- 11
- Qwen2.5 7B Instruct only
- 5
- Like-for-like categories
- 2 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 Granite 4.0 Micro has a AA Intelligence Index result, 5.1.
Directional only
Long documents
Listed context window
Granite 4.0 Micro
Granite 4.0 Micro takes 131K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Granite 4.0 Micro
Granite 4.0 Micro $0.0001, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- Granite 4.0 Micro not benchmarked, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- Reasoning, 7.2 points apart on average over 1 shared benchmark.
- Widest single gap
- GPQA Diamond (Epoch AI run): Granite 4.0 Micro 28.3% against Qwen2.5 7B Instruct 35.5%, 7.2 points apart on a 0 to 100 scale.
- Output price
- Granite 4.0 Micro $0.112 against Qwen2.5 7B Instruct $0.20 per million output tokens, 1.8x apart.
- Context window
- Granite 4.0 Micro takes 131K tokens, Qwen2.5 7B Instruct 33K.
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 | Qwen2.5 7B Instruct | Ties |
|---|---|---|---|---|
| Reasoning | 0 | 1 | ||
| Math | 1 | 0 | ||
| All shared boards | 1 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards, none shared
| Benchmark | Granite 4.0 Micro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 18.0% | Not measured | Only Granite 4.0 Micro measured |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | Not measured | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Granite 4.0 Micro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.1 | Not measured | Only Granite 4.0 Micro measured |
| Epoch Capabilities Index | Not measured | 118.5 | Only Qwen2.5 7B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Granite 4.0 Micro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 12.6% | Not measured | Only Granite 4.0 Micro measured |
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Granite 4.0 Micro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 33.6% | Not measured | Only Granite 4.0 Micro measured |
| Humanity's Last Exam (Artificial Analysis run) | 5.0% | Not measured | Only Granite 4.0 Micro measured |
| GPQA Diamond (Epoch AI run) | 28.3% | 35.5% | Qwen2.5 7B Instruct |
| Neither model has a result on the other 20 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Granite 4.0 Micro | Qwen2.5 7B Instruct | 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% | 2.5% | Granite 4.0 Micro |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Granite 4.0 Micro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 44.7% | Not measured | Only Granite 4.0 Micro measured |
| MMLU | Not measured | 72.9% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 18 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Granite 4.0 Micro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | Granite 4.0 Micro | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| IFBench | 24.8% | Not measured | Only Granite 4.0 Micro measured |
| Long context reasoning (AA-LCR) | 7.0% | Not measured | Only Granite 4.0 Micro measured |
| DTBench | Not measured | 47.7% | Only Qwen2.5 7B Instruct measured |
| LMCA | Not measured | 6.4% | Only Qwen2.5 7B Instruct 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 | Granite 4.0 Micro | Qwen2.5 7B Instruct | 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 | Qwen2.5 7B Instruct |
|---|---|---|
| Maker | IBM Granite | Qwen |
| Weights | Open weights | Open weights |
| Released | October 20, 2025 | October 16, 2024 |
| Context window | 131K | 33K |
| Input $/Mtok | $0.017 | $0.10 |
| Output $/Mtok | $0.112 | $0.20 |
| 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 Qwen2.5 7B Instruct 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 1.8x cheaper ($0.112 vs $0.20 per million) and a larger context window (131K against 33K tokens).
Which is cheaper, Granite 4.0 Micro or Qwen2.5 7B Instruct?
Granite 4.0 Micro costs $0.017 per million input tokens and $0.112 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Granite 4.0 Micro or Qwen2.5 7B Instruct?
Granite 4.0 Micro, with 131K tokens against 33K.
How many benchmarks is the Granite 4.0 Micro vs Qwen2.5 7B Instruct comparison based on?
2 benchmarks have a result for both models as of October 22, 2026. Granite 4.0 Micro's coding index rests on 0 independent benchmarks and Qwen2.5 7B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.