Llama 3.3 70B Instruct vs Mistral Medium 3
Mistral Medium 3 leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 9.0 to 7.7, and Llama 3.3 70B Instruct costs 2.8x less per output token ($0.72 vs $2.00 per million). Updated October 21, 2026; 41 benchmarks have a result for both.
Llama 3.3 70B Instruct or Mistral Medium 3: 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 2.8x cheaper ($0.72 vs $2.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 41
- Llama 3.3 70B Instruct only
- 11
- Mistral Medium 3 only
- 16
- 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
Mistral Medium 3
Mistral Medium 3 leads, 9.0 against 7.7.
Same board, both measured
Web design
Design Arena: websites
No call
Only Mistral Medium 3 has a Design Arena: websites result, 1079.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct $0.0008, Mistral Medium 3 $0.0014 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, Mistral Medium 3 not benchmarked.
- Widest category
- Math, 18.5 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena Text: Japanese: Llama 3.3 70B Instruct 1150 against Mistral Medium 3 1316, 27.6 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against Mistral Medium 3 $2.00 per million output tokens, 2.8x apart.
- Context window
- Both take 131K tokens.
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 | Mistral Medium 3 | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| General | 1 | 19 | ||
| All shared boards | 2 | 39 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
| Benchmark | Llama 3.3 70B Instruct | Mistral Medium 3 | Leader |
|---|---|---|---|
| AA Coding Index | 11.9 | Not measured | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 28.8% | 40.0% | Mistral Medium 3 |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | 3.8% | Mistral Medium 3 |
| WeirdML | 14.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | 1268 | 1385 | Mistral Medium 3 |
| LMArena Text: software and IT | 1273 | 1385 | Mistral Medium 3 |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.3 70B Instruct | Mistral Medium 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 9.0 | Mistral Medium 3 |
| Epoch Capabilities Index | 127.3 | 134.0 | Mistral Medium 3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Llama 3.3 70B Instruct | Mistral Medium 3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 26.6% | 24.3% | Llama 3.3 70B Instruct |
| 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 index05
| Benchmark | Llama 3.3 70B Instruct | Mistral Medium 3 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.8% | 57.8% | Mistral Medium 3 |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | 4.1% | Mistral Medium 3 |
| 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 | 1364 | Mistral Medium 3 |
| LMArena Text: expert | 1225 | 1342 | Mistral Medium 3 |
| LMArena Text: hard prompts in English | 1277 | 1374 | Mistral Medium 3 |
| LMArena Vision: diagrams | Not measured | 1155 | Only Mistral Medium 3 measured |
| LMArena Vision: homework | Not measured | 1165 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.3 70B Instruct | Mistral Medium 3 | Leader |
|---|---|---|---|
| AA Math Index | 7.7 | 30.3 | Mistral Medium 3 |
| AIME 2025 (Artificial Analysis run) | 7.7% | 30.3% | Mistral Medium 3 |
| 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 | 1353 | Mistral Medium 3 |
| LMArena Text: mathematical work | 1267 | 1354 | Mistral Medium 3 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Llama 3.3 70B Instruct | Mistral Medium 3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.3% | 76.0% | Mistral Medium 3 |
| MMLU | 86.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1284 | 1381 | Mistral Medium 3 |
| LMArena Text: medicine and healthcare | 1275 | 1381 | Mistral Medium 3 |
| LMArena Text: legal and government | 1284 | 1384 | Mistral Medium 3 |
| LMArena Text: business and finance | 1243 | 1352 | Mistral Medium 3 |
| Neither model has a result on the other 12 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Mistral Medium 3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1079 | Only Mistral Medium 3 measured |
| Design Arena: UI components | Not measured | 1024 | Only Mistral Medium 3 measured |
| Design Arena: game dev | Not measured | 1018 | Only Mistral Medium 3 measured |
| Design Arena: data viz | Not measured | 1034 | Only Mistral Medium 3 measured |
| Design Arena: 3D | Not measured | 1084 | Only Mistral Medium 3 measured |
| Design Arena: all code categories | Not measured | 1069 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index119
| Benchmark | Llama 3.3 70B Instruct | Mistral Medium 3 | Leader |
|---|---|---|---|
| IFBench | 47.1% | 39.3% | Llama 3.3 70B Instruct |
| Long context reasoning (AA-LCR) | 15.7% | 31.3% | Mistral Medium 3 |
| DTBench | 59.5% | 62.3% | Mistral Medium 3 |
| 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 | 1370 | Mistral Medium 3 |
| LMArena Text: instruction following | 1242 | 1338 | Mistral Medium 3 |
| LMArena Text: creative writing | 1250 | 1343 | Mistral Medium 3 |
| LMArena Vision | Not measured | 1157 | Only Mistral Medium 3 measured |
| LMArena Text: English | 1303 | 1385 | Mistral Medium 3 |
| LMArena Text: non-English | 1236 | 1353 | Mistral Medium 3 |
| LMArena Text: Chinese | 1217 | 1374 | Mistral Medium 3 |
| LMArena Text: German | 1251 | 1377 | Mistral Medium 3 |
| LMArena Text: Japanese | 1150 | 1316 | Mistral Medium 3 |
| LMArena Text: Korean | 1142 | 1304 | Mistral Medium 3 |
| LMArena Text: French | 1282 | 1368 | Mistral Medium 3 |
| LMArena Text: Spanish | 1269 | 1364 | Mistral Medium 3 |
| LMArena Text: Russian | 1252 | 1359 | Mistral Medium 3 |
| LMArena Text: Polish | 1259 | 1371 | Mistral Medium 3 |
| LMArena Text: multi-turn | 1280 | 1384 | Mistral Medium 3 |
| LMArena Text: longer queries | 1255 | 1360 | Mistral Medium 3 |
| LMArena Text: writing and language | 1255 | 1352 | Mistral Medium 3 |
| LMArena Text: entertainment and media | 1241 | 1335 | Mistral Medium 3 |
| LMArena Vision: English | Not measured | 1172 | Only Mistral Medium 3 measured |
| LMArena Vision: Chinese | Not measured | 1161 | Only Mistral Medium 3 measured |
| LMArena Vision: reading text | Not measured | 1154 | Only Mistral Medium 3 measured |
| LMArena Vision: humor | Not measured | 1145 | Only Mistral Medium 3 measured |
| LMArena Vision: captioning | Not measured | 1094 | Only Mistral Medium 3 measured |
| LMArena Vision: recognizing things | Not measured | 1141 | Only Mistral Medium 3 measured |
| LMArena Vision: creative writing | Not measured | 1146 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Mistral Medium 3 | 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 | Mistral Medium 3 |
|---|---|---|
| Maker | Meta Llama | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | December 6, 2024 | May 7, 2025 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.45 | $0.40 |
| Output $/Mtok | $0.72 | $2.00 |
| 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 Mistral Medium 3 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 2.8x cheaper ($0.72 vs $2.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, Llama 3.3 70B Instruct or Mistral Medium 3?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; Mistral Medium 3 costs $0.40 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.3 70B Instruct or Mistral Medium 3?
Neither: both take 131K tokens.
How many benchmarks is the Llama 3.3 70B Instruct vs Mistral Medium 3 comparison based on?
41 benchmarks have a result for both models as of October 21, 2026. Llama 3.3 70B Instruct's coding index rests on 0 independent benchmarks and Mistral Medium 3's on 0. Where only one model has a result, the page says so instead of naming a winner.