Model comparison
Granite 4.0 Micro vs Llama-3.3-70B-Instruct
Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.3 to 28.8 on the Noometry Index. Granite 4.0 Micro costs 3.8× less per token, which makes it the better buy when Llama-3.3-70B-Instruct's lead doesn't matter for your workload.
Last verified . 2 shared benchmarks.
MetaSummary
- They share 2 benchmarks with published results for both. Granite 4.0 Micro scores higher in 1 category and Llama-3.3-70B-Instruct in 4 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where Llama-3.3-70B-Instruct leads 30.5 to 9.9.
- The biggest single-benchmark swing is GPQA Diamond: 28.3% for Granite 4.0 Micro and 47.4% for Llama-3.3-70B-Instruct.
- Granite 4.0 Micro is cheaper at $0.017 / $0.11 per million input/output tokens, against $0.10 / $0.32 for Llama-3.3-70B-Instruct.
- Granite 4.0 Micro accepts more context: 131K tokens versus 128K.
Side by side
| Granite 4.0 Micro | Llama-3.3-70B-Instruct | |
|---|---|---|
| Provider | IBM | Meta |
| Noometry Index | 28.8 | 30.3 |
| Released | 2025-10-02 | 2024-12-06 |
| Weights | Open | Open |
| Context window | 131K | 128K |
| Max output | 118K | 4K |
| Input $ / M tokens | $0.017 | $0.10 |
| Output $ / M tokens | $0.11 | $0.32 |
| Results tracked | 8 | 43 |
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Category by category
Coding Not comparable
Granite 4.0 Micro: —, Llama-3.3-70B-Instruct: 30.6 (#284)
| Benchmark | Granite 4.0 Micro | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | — | 26% |
| WeirdML | — | 14.4% |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| LMArena Coding | — | 1268 |
| BigCodeBench Complete | — | 57.5% |
Agentic & Tool Use Not comparable
Granite 4.0 Micro: —, Llama-3.3-70B-Instruct: 25.6 (#100)
| Benchmark | Granite 4.0 Micro | Llama-3.3-70B-Instruct |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 31.9% |
| BALROG | — | 23% |
Reasoning Granite 4.0 Micro leads
Granite 4.0 Micro: 18.5 (#261), Llama-3.3-70B-Instruct: 13.7 (#325)
| Benchmark | Granite 4.0 Micro | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | — | 19.9% |
| CritPt | — | 0% |
| Chess Puzzles | 0% | — |
| LiveBench Reasoning | — | 50.8% |
| LMArena Hard Prompts | — | 1257 |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| Epoch Capabilities Index | — | 127.32 |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math Llama-3.3-70B-Instruct leads
Granite 4.0 Micro: 11.2 (#301), Llama-3.3-70B-Instruct: 14.3 (#292)
| Benchmark | Granite 4.0 Micro | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 2.8% | 5.1% |
| Omni-MATH | 20.9% | — |
| LiveBench Math | — | 42.2% |
| LMArena Math | — | 1268 |
| MATH Level 5 | — | 41.6% |
Knowledge Llama-3.3-70B-Instruct leads
Granite 4.0 Micro: 9.9 (#299), Llama-3.3-70B-Instruct: 30.5 (#220)
| Benchmark | Granite 4.0 Micro | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 28.3% | 47.4% |
| MMLU-Pro | 39.5% | — |
| Confabulations | — | 22.8% |
| Vectara Hallucination Rate | — | 4.1% |
| GPQA (HELM) | 30.7% | — |
| LMArena Expert | — | 1225 |
| MMLU | — | 86.3% |
Multilingual Not comparable
Granite 4.0 Micro: —, Llama-3.3-70B-Instruct: 40.1 (#215)
| Benchmark | Granite 4.0 Micro | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | — | 1236 |
| LMArena Chinese | — | 1217 |
| LMArena French | — | 1280 |
| LMArena German | — | 1251 |
| LMArena Japanese | — | 1149 |
| LMArena Korean | — | 1142 |
| LMArena Russian | — | 1252 |
| LMArena Spanish | — | 1269 |
Instruction Following Llama-3.3-70B-Instruct leads
Granite 4.0 Micro: 70.0 (#164), Llama-3.3-70B-Instruct: 71.3 (#152)
| Benchmark | Granite 4.0 Micro | Llama-3.3-70B-Instruct |
|---|---|---|
| LiveBench Instruction Following | — | 82.7% |
| IFEval | 84.9% | — |
| LMArena Instruction Following | — | 1242 |
Long Context Not comparable
Granite 4.0 Micro: —, Llama-3.3-70B-Instruct: 26.4 (#290)
| Benchmark | Granite 4.0 Micro | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | — | 33.3% |
| LMArena Longer Query | — | 1256 |
Writing & Preference Llama-3.3-70B-Instruct leads
Granite 4.0 Micro: 46.7 (#212), Llama-3.3-70B-Instruct: 47.8 (#203)
| Benchmark | Granite 4.0 Micro | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | — | 1274 |
| LMArena Creative Writing | — | 1250 |
| WildBench | 67% | — |
| LMArena Multi-Turn | — | 1279 |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Granite 4.0 Micro better than Llama-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is the stronger model overall, scoring 30.3 to 28.8 on the Noometry Index. Granite 4.0 Micro costs 3.8× less per token, which makes it the better buy when Llama-3.3-70B-Instruct's lead doesn't matter for your workload.
Which is cheaper, Granite 4.0 Micro or Llama-3.3-70B-Instruct?
Granite 4.0 Micro is cheaper. It lists at $0.017 per million input tokens and $0.11 per million output tokens; Llama-3.3-70B-Instruct lists at $0.10 and $0.32.
Which has the bigger context window?
Granite 4.0 Micro does, with 131K tokens against 128K.
How many benchmarks do Granite 4.0 Micro and Llama-3.3-70B-Instruct share?
2 benchmarks have published results for both models. Granite 4.0 Micro has 8 scored results on Noometry and Llama-3.3-70B-Instruct has 43.