Llama 3.3 70B Instruct vs MiniMax M1
MiniMax M1 leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 11.7 to 7.7, and Llama 3.3 70B Instruct costs 3.1x less per output token ($0.72 vs $2.20 per million). Updated October 22, 2026; 29 benchmarks have a result for both.
Llama 3.3 70B Instruct or MiniMax M1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.3 70B Instruct for output tokens 3.1x cheaper ($0.72 vs $2.20 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 29
- Llama 3.3 70B Instruct only
- 23
- MiniMax M1 only
- 0
- Like-for-like categories
- 6 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
MiniMax M1
MiniMax M1 leads, 11.7 against 7.7.
Same board, both measured
Long documents
Listed context window
MiniMax M1
MiniMax M1 takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct $0.0008, MiniMax M1 $0.0015 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, MiniMax M1 not benchmarked.
- Widest category
- Math, 15.8 points apart on average over 2 shared benchmarks.
- Widest single gap
- LMArena Text: Chinese: Llama 3.3 70B Instruct 1217 against MiniMax M1 1360, 23.8 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against MiniMax M1 $2.20 per million output tokens, 3.1x apart.
- Context window
- Llama 3.3 70B Instruct takes 131K tokens, MiniMax M1 1M.
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 | Llama 3.3 70B Instruct | Bars | MiniMax M1 | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 17 | ||
| All shared boards | 0 | 29 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Llama 3.3 70B Instruct | MiniMax M1 | Leader |
|---|---|---|---|
| AA Coding Index | 11.9 | Not measured | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 28.8% | Not measured | Only Llama 3.3 70B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| WeirdML | 14.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | 1268 | 1359 | MiniMax M1 |
| LMArena Text: software and IT | 1273 | 1359 | MiniMax M1 |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.3 70B Instruct | MiniMax M1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 11.7 | MiniMax M1 |
| Epoch Capabilities Index | 127.3 | Not measured | Only Llama 3.3 70B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | MiniMax M1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 26.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| BALROG | 23.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| Berkeley Function Calling Leaderboard (BFCL) | 31.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Llama 3.3 70B Instruct | MiniMax M1 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.8% | Not measured | Only Llama 3.3 70B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| GPQA Diamond (Epoch AI run) | 47.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| SimpleBench | 19.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1257 | 1339 | MiniMax M1 |
| LMArena Text: expert | 1225 | 1317 | MiniMax M1 |
| LMArena Text: hard prompts in English | 1277 | 1353 | MiniMax M1 |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.3 70B Instruct | MiniMax M1 | Leader |
|---|---|---|---|
| AA Math Index | 7.7 | Not measured | Only Llama 3.3 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 7.7% | Not measured | Only Llama 3.3 70B Instruct measured |
| OTIS Mock AIME | 5.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| MATH level 5 | 41.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | 1267 | 1361 | MiniMax M1 |
| LMArena Text: mathematical work | 1267 | 1363 | MiniMax M1 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.3 70B Instruct | MiniMax M1 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| MMLU | 86.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1284 | 1354 | MiniMax M1 |
| LMArena Text: medicine and healthcare | 1275 | 1355 | MiniMax M1 |
| LMArena Text: legal and government | 1284 | 1337 | MiniMax M1 |
| LMArena Text: business and finance | 1243 | 1319 | MiniMax M1 |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | MiniMax M1 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
| Benchmark | Llama 3.3 70B Instruct | MiniMax M1 | Leader |
|---|---|---|---|
| IFBench | 47.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| Long context reasoning (AA-LCR) | 15.7% | Not measured | Only Llama 3.3 70B Instruct measured |
| DTBench | 59.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMCA | 17.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| Fiction.LiveBench | 33.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text | 1274 | 1343 | MiniMax M1 |
| LMArena Text: instruction following | 1242 | 1312 | MiniMax M1 |
| LMArena Text: creative writing | 1250 | 1297 | MiniMax M1 |
| LMArena Text: English | 1303 | 1363 | MiniMax M1 |
| LMArena Text: non-English | 1236 | 1319 | MiniMax M1 |
| LMArena Text: Chinese | 1217 | 1360 | MiniMax M1 |
| LMArena Text: German | 1251 | 1350 | MiniMax M1 |
| LMArena Text: Japanese | 1150 | 1217 | MiniMax M1 |
| LMArena Text: Korean | 1142 | 1266 | MiniMax M1 |
| LMArena Text: French | 1282 | 1370 | MiniMax M1 |
| LMArena Text: Spanish | 1269 | 1353 | MiniMax M1 |
| LMArena Text: Russian | 1252 | 1329 | MiniMax M1 |
| LMArena Text: Polish | 1259 | 1329 | MiniMax M1 |
| LMArena Text: multi-turn | 1280 | 1335 | MiniMax M1 |
| LMArena Text: longer queries | 1255 | 1326 | MiniMax M1 |
| LMArena Text: writing and language | 1255 | 1311 | MiniMax M1 |
| LMArena Text: entertainment and media | 1241 | 1303 | MiniMax M1 |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | MiniMax M1 | 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
Llama 3.3 70B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.3 70B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.3 70B Instruct 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 | Meta Llama 3.3 70B Instruct | MiniMax M1 |
|---|---|---|
| Maker | Meta Llama | MiniMax |
| Weights | Open weights | No public weights listed |
| Released | December 6, 2024 | June 17, 2025 |
| Context window | 131K | 1M |
| Input $/Mtok | $0.45 | $0.40 |
| Output $/Mtok | $0.72 | $2.20 |
| 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 Llama 3.3 70B Instruct better than MiniMax M1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.3 70B Instruct for output tokens 3.1x cheaper ($0.72 vs $2.20 per million) and work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, Llama 3.3 70B Instruct or MiniMax M1?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; MiniMax M1 costs $0.40 and $2.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.3 70B Instruct or MiniMax M1?
MiniMax M1, with 1M tokens against 131K.
How many benchmarks is the Llama 3.3 70B Instruct vs MiniMax M1 comparison based on?
29 benchmarks have a result for both models as of October 22, 2026. Llama 3.3 70B Instruct's coding index rests on 0 independent benchmarks and MiniMax M1's on 0. Where only one model has a result, the page says so instead of naming a winner.