Llama 3.3 70B Instruct vs Mistral Large
Updated October 21, 2026. 36 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
Llama 3.3 70B Instruct has the edge where output tokens cost 8.3x less, and where the context window is larger, 131K against 128K tokens.
- Shared results
- 36
- Llama 3.3 70B Instruct only
- 15
- Mistral Large only
- 8
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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 5.8.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Mistral Large has a GPQA Diamond result, 47.7%.
Directional only
Math
AIME
No call
Only Mistral Large has a AIME result, 9.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Mistral Large has a MMLU Pro result, 69.7%.
Directional only
Long documents
Listed context window
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.3 70B Instruct
Llama 3.3 70B Instruct $0.0008, Mistral Large $0.0050 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, Mistral Large not benchmarked.
- Widest category
- Knowledge, 7.8 points apart on average over 6 shared benchmarks.
- Widest single gap
- MMLU Pro (Artificial Analysis run): Llama 3.3 70B Instruct 71.3% against Mistral Large 51.5%, 19.8 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against Mistral Large $6.00 per million output tokens, 8.3x apart.
- Context window
- Llama 3.3 70B Instruct takes 131K tokens, Mistral Large 128K.
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 Large | Ties |
|---|---|---|---|---|
| Coding | 1 | 2 | ||
| Intelligence | 2 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 6 | 0 | ||
| General | 10 | 8 | ||
| All shared boards | 26 | 10 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index12
| Benchmark | Llama 3.3 70B Instruct | Mistral Large | Leader |
|---|---|---|---|
| AA Coding Index | 11.9 | Not measured | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Vals AI) | Not measured | 37.1% | Only Mistral Large measured |
| LiveCodeBench (Artificial Analysis run) | 28.8% | 17.8% | Llama 3.3 70B Instruct |
| 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 | 1275 | Mistral Large |
| LMArena Text: software and IT | 1273 | 1274 | Mistral Large |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Llama 3.3 70B Instruct | Mistral Large | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 5.8 | Llama 3.3 70B Instruct |
| Epoch Capabilities Index | 127.3 | 122.1 | Llama 3.3 70B Instruct |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Mistral Large | 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 |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Llama 3.3 70B Instruct | Mistral Large | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 47.7% | Only Mistral Large measured |
| GPQA Diamond (Artificial Analysis run) | 49.8% | 35.1% | Llama 3.3 70B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | 3.5% | Llama 3.3 70B Instruct |
| 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 | 1257 | Llama 3.3 70B Instruct |
| LMArena Text: expert | 1225 | 1208 | Llama 3.3 70B Instruct |
| LMArena Text: hard prompts in English | 1277 | 1268 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Llama 3.3 70B Instruct | Mistral Large | Leader |
|---|---|---|---|
| AIME | Not measured | 9.2% | Only Mistral Large measured |
| MATH 500 (Vals AI) | Not measured | 74.4% | Only Mistral Large measured |
| MGSM (Vals AI) | Not measured | 87.2% | Only Mistral Large measured |
| 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 | 1261 | Llama 3.3 70B Instruct |
| LMArena Text: mathematical work | 1267 | 1257 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Llama 3.3 70B Instruct | Mistral Large | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 69.7% | Only Mistral Large measured |
| TaxEval (Vals AI) | Not measured | 63.8% | Only Mistral Large measured |
| MedQA (Vals AI) | Not measured | 76.2% | Only Mistral Large measured |
| MMLU Pro (Artificial Analysis run) | 71.3% | 51.5% | Llama 3.3 70B Instruct |
| MMLU | 86.3% | 68.8% | Llama 3.3 70B Instruct |
| LMArena Text: science | 1284 | 1260 | Llama 3.3 70B Instruct |
| LMArena Text: medicine and healthcare | 1275 | 1255 | Llama 3.3 70B Instruct |
| LMArena Text: legal and government | 1284 | 1275 | Llama 3.3 70B Instruct |
| LMArena Text: business and finance | 1243 | 1238 | Llama 3.3 70B Instruct |
| Neither model has a result on the other 8 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Mistral Large | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index108
| Benchmark | Llama 3.3 70B Instruct | Mistral Large | 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% | 60.8% | Mistral Large |
| 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 | 1265 | Llama 3.3 70B Instruct |
| LMArena Text: instruction following | 1242 | 1249 | Mistral Large |
| LMArena Text: creative writing | 1250 | 1242 | Llama 3.3 70B Instruct |
| LMArena Text: English | 1303 | 1284 | Llama 3.3 70B Instruct |
| LMArena Text: non-English | 1236 | 1235 | Llama 3.3 70B Instruct |
| LMArena Text: Chinese | 1217 | 1239 | Mistral Large |
| LMArena Text: German | 1251 | 1242 | Llama 3.3 70B Instruct |
| LMArena Text: Japanese | 1150 | 1170 | Mistral Large |
| LMArena Text: Korean | 1142 | 1202 | Mistral Large |
| LMArena Text: French | 1282 | 1325 | Mistral Large |
| LMArena Text: Spanish | 1269 | 1268 | Llama 3.3 70B Instruct |
| LMArena Text: Russian | 1252 | 1252 | Llama 3.3 70B Instruct |
| LMArena Text: Polish | 1259 | 1157 | Llama 3.3 70B Instruct |
| LMArena Text: multi-turn | 1280 | 1260 | Llama 3.3 70B Instruct |
| LMArena Text: longer queries | 1255 | 1261 | Mistral Large |
| LMArena Text: writing and language | 1255 | 1261 | Mistral Large |
| LMArena Text: entertainment and media | 1241 | 1230 | Llama 3.3 70B Instruct |
| 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 | Mistral Large | 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 Large |
|---|---|---|
| Maker | Meta Llama | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | December 6, 2024 | February 26, 2024 |
| Context window | 131K | 128K |
| Input $/Mtok | $0.45 | $2.00 |
| Output $/Mtok | $0.72 | $6.00 |
| 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 Large for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Llama 3.3 70B Instruct has the edge where output tokens cost 8.3x less, and where the context window is larger, 131K against 128K tokens.
Which is cheaper, Llama 3.3 70B Instruct or Mistral Large?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; Mistral Large costs $2.00 and $6.00. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.3 70B Instruct or Mistral Large?
Llama 3.3 70B Instruct, with 131K tokens against 128K.
How many benchmarks is the Llama 3.3 70B Instruct vs Mistral Large comparison based on?
36 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 Large's on 0. Where only one model has a result, the page says so instead of naming a winner.