Granite 4.0 Micro vs Llama 3.1 Euryale 70B v2.2
Granite 4.0 Micro costs 7.6x less per output token than Llama 3.1 Euryale 70B v2.2 ($0.112 vs $0.85 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.112
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.85
Granite 4.0 Micro or Llama 3.1 Euryale 70B v2.2: 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 7.6x cheaper ($0.112 vs $0.85 per million).
No benchmark has tested both yet. Granite 4.0 Micro has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and AA Intelligence Index; Llama 3.1 Euryale 70B v2.2 has none yet. 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 Granite 4.0 Micro has a AA Intelligence Index result, 5.1.
Directional only
Long documents
Listed context window
Llama 3.1 Euryale 70B v2.2
Llama 3.1 Euryale 70B v2.2 takes 131K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Granite 4.0 Micro
Granite 4.0 Micro $0.0001, Llama 3.1 Euryale 70B v2.2 $0.0013 per turn.
List prices
At a glance
- Coding index
- Granite 4.0 Micro not benchmarked, Llama 3.1 Euryale 70B v2.2 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
- Granite 4.0 Micro $0.112 against Llama 3.1 Euryale 70B v2.2 $0.85 per million output tokens, 7.6x apart.
- Context window
- Granite 4.0 Micro takes 131K tokens, Llama 3.1 Euryale 70B v2.2 131K.
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.
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards measured
| Benchmark | Granite 4.0 Micro | Llama 3.1 Euryale 70B v2.2 | 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 index1 board measured
| Benchmark | Granite 4.0 Micro | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.1 | Not measured | Only Granite 4.0 Micro measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Granite 4.0 Micro | Llama 3.1 Euryale 70B v2.2 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 12.6% | Not measured | 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 index3 boards measured
| Benchmark | Granite 4.0 Micro | Llama 3.1 Euryale 70B v2.2 | 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% | 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 measured
| Benchmark | Granite 4.0 Micro | Llama 3.1 Euryale 70B v2.2 | 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 measured
| Benchmark | Granite 4.0 Micro | Llama 3.1 Euryale 70B v2.2 | 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Granite 4.0 Micro | Llama 3.1 Euryale 70B v2.2 | 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 |
| Neither model has a result on the other 46 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 | Sao10K Llama 3.1 Euryale 70B v2.2 |
|---|---|---|
| Maker | IBM Granite | Sao10K |
| Weights | Open weights | Open weights |
| Released | October 20, 2025 | August 28, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.017 | $0.85 |
| Output $/Mtok | $0.112 | $0.85 |
| 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 Llama 3.1 Euryale 70B v2.2 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 7.6x cheaper ($0.112 vs $0.85 per million).
Which is cheaper, Granite 4.0 Micro or Llama 3.1 Euryale 70B v2.2?
Granite 4.0 Micro costs $0.017 per million input tokens and $0.112 per million output tokens; Llama 3.1 Euryale 70B v2.2 costs $0.85 and $0.85. Cost per solved task is known for neither of them.
Which has the bigger context window, Granite 4.0 Micro or Llama 3.1 Euryale 70B v2.2?
Llama 3.1 Euryale 70B v2.2, with 131K tokens against 131K.
Has any benchmark tested both Granite 4.0 Micro and Llama 3.1 Euryale 70B v2.2?
No benchmark has tested both yet. Granite 4.0 Micro has results on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and AA Intelligence Index; Llama 3.1 Euryale 70B v2.2 has none yet. Compare them on price and context instead. Data as of October 22, 2026.