Granite 4.0 Micro vs Llama 3.3 70B Instruct
Llama 3.3 70B Instruct leads Granite 4.0 Micro on the Artificial Analysis Intelligence Index, 7.7 to 5.1, and Granite 4.0 Micro costs 6.4x less per output token ($0.112 vs $0.72 per million). Updated October 22, 2026; 13 benchmarks have a result for both.
Granite 4.0 Micro or Llama 3.3 70B 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 6.4x cheaper ($0.112 vs $0.72 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 13
- Granite 4.0 Micro only
- 0
- Llama 3.3 70B Instruct only
- 39
- Like-for-like categories
- 7 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
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct leads, 7.7 against 5.1.
Same board, both measured
Long documents
Listed context window
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct 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 3.3 70B Instruct $0.0008 per turn.
List prices
At a glance
- Coding index
- Granite 4.0 Micro not benchmarked, Llama 3.3 70B Instruct not benchmarked.
- Widest category
- Knowledge, 26.6 points apart on average over 1 shared benchmark.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Granite 4.0 Micro 44.7% against Llama 3.3 70B Instruct 71.3%, 26.6 points apart on a 0 to 100 scale.
- Output price
- Granite 4.0 Micro $0.112 against Llama 3.3 70B Instruct $0.72 per million output tokens, 6.4x apart.
- Context window
- Granite 4.0 Micro takes 131K tokens, Llama 3.3 70B Instruct 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 | Granite 4.0 Micro | Bars | Llama 3.3 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 1 | 2 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 1 | 12 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Granite 4.0 Micro | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 11.9 | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 18.0% | 28.8% | Llama 3.3 70B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | 3.0% | Llama 3.3 70B Instruct |
| WeirdML | Not measured | 14.4% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | Not measured | 1268 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: software and IT | Not measured | 1273 | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Granite 4.0 Micro | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.1 | 7.7 | Llama 3.3 70B Instruct |
| Epoch Capabilities Index | Not measured | 127.3 | Only Llama 3.3 70B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Granite 4.0 Micro | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 12.6% | 26.6% | Llama 3.3 70B Instruct |
| BALROG | Not measured | 23.0% | Only Llama 3.3 70B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 31.9% | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | Granite 4.0 Micro | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 33.6% | 49.8% | Llama 3.3 70B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 5.0% | 3.6% | Granite 4.0 Micro |
| GPQA Diamond (Epoch AI run) | 28.3% | 47.4% | Llama 3.3 70B Instruct |
| SimpleBench | Not measured | 19.9% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | Not measured | 1257 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: expert | Not measured | 1225 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1277 | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Granite 4.0 Micro | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | 7.7 | Llama 3.3 70B Instruct |
| AIME 2025 (Artificial Analysis run) | 6.0% | 7.7% | Llama 3.3 70B Instruct |
| OTIS Mock AIME | 2.8% | 5.1% | Llama 3.3 70B Instruct |
| MATH level 5 | Not measured | 41.6% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | Not measured | 1267 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1267 | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Granite 4.0 Micro | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 44.7% | 71.3% | Llama 3.3 70B Instruct |
| MMLU | Not measured | 86.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | Not measured | 1284 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1275 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: legal and government | Not measured | 1284 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: business and finance | Not measured | 1243 | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Granite 4.0 Micro | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Granite 4.0 Micro | Llama 3.3 70B Instruct | Leader |
|---|---|---|---|
| IFBench | 24.8% | 47.1% | Llama 3.3 70B Instruct |
| Long context reasoning (AA-LCR) | 7.0% | 15.7% | Llama 3.3 70B Instruct |
| DTBench | Not measured | 59.5% | Only Llama 3.3 70B Instruct measured |
| LMCA | Not measured | 17.5% | Only Llama 3.3 70B Instruct measured |
| Fiction.LiveBench | Not measured | 33.3% | Only Llama 3.3 70B Instruct measured |
| LMArena Text | Not measured | 1274 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: instruction following | Not measured | 1242 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: creative writing | Not measured | 1250 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: English | Not measured | 1303 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: non-English | Not measured | 1236 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Chinese | Not measured | 1217 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: German | Not measured | 1251 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Japanese | Not measured | 1150 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Korean | Not measured | 1142 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: French | Not measured | 1282 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Spanish | Not measured | 1269 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Russian | Not measured | 1252 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Polish | Not measured | 1259 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1280 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: longer queries | Not measured | 1255 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: writing and language | Not measured | 1255 | Only Llama 3.3 70B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1241 | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Granite 4.0 Micro | Llama 3.3 70B 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 | Meta Llama 3.3 70B Instruct |
|---|---|---|
| Maker | IBM Granite | Meta Llama |
| Weights | Open weights | Open weights |
| Released | October 20, 2025 | December 6, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.017 | $0.45 |
| Output $/Mtok | $0.112 | $0.72 |
| 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 3.3 70B 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 6.4x cheaper ($0.112 vs $0.72 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Granite 4.0 Micro or Llama 3.3 70B Instruct?
Granite 4.0 Micro costs $0.017 per million input tokens and $0.112 per million output tokens; Llama 3.3 70B Instruct costs $0.45 and $0.72. Cost per solved task is known for neither of them.
Which has the bigger context window, Granite 4.0 Micro or Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct, with 131K tokens against 131K.
How many benchmarks is the Granite 4.0 Micro vs Llama 3.3 70B Instruct comparison based on?
13 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 Llama 3.3 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.