Llama 3.3 70B Instruct vs Mistral Large 2407
Llama 3.3 70B Instruct leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 7.7 to 7.6, and Llama 3.3 70B Instruct costs 8.3x less per output token ($0.72 vs $6.00 per million). Updated October 21, 2026; 16 benchmarks have a result for both.
Llama 3.3 70B Instruct or Mistral Large 2407: 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 8.3x cheaper ($0.72 vs $6.00 per million) and work measured by τ²-Bench Telecom or GPQA Diamond (Epoch AI run), where it leads.
- Shared results
- 16
- Llama 3.3 70B Instruct only
- 36
- Mistral Large 2407 only
- 1
- 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 7.6.
Same board, both measured
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 Large 2407 $0.0050 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- General, 10.3 points apart on average over 3 shared benchmarks.
- Widest single gap
- IFBench: Llama 3.3 70B Instruct 47.1% against Mistral Large 2407 31.6%, 15.4 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against Mistral Large 2407 $6.00 per million output tokens, 8.3x 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 Large 2407 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 2 | ||
| Math | 2 | 2 | ||
| Knowledge | 2 | 0 | ||
| General | 2 | 1 | ||
| All shared boards | 10 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Llama 3.3 70B Instruct | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Coding Index | 11.9 | Not measured | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 28.8% | 26.7% | 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 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: software and IT | 1273 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Llama 3.3 70B Instruct | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 7.6 | Llama 3.3 70B Instruct |
| Epoch Capabilities Index | 127.3 | Not measured | Only Llama 3.3 70B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.3 70B Instruct | Mistral Large 2407 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 26.6% | 33.0% | Mistral Large 2407 |
| 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 index22
| Benchmark | Llama 3.3 70B Instruct | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.8% | 47.2% | Llama 3.3 70B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | 2.9% | Llama 3.3 70B Instruct |
| GPQA Diamond (Epoch AI run) | 47.4% | 49.0% | Mistral Large 2407 |
| SimpleBench | 19.9% | 22.5% | Mistral Large 2407 |
| LMArena Text: hard prompts | 1257 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: expert | 1225 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts in English | 1277 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | Llama 3.3 70B Instruct | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Math Index | 7.7 | 0.0 | Llama 3.3 70B Instruct |
| AIME 2025 (Artificial Analysis run) | 7.7% | 0.0% | Llama 3.3 70B Instruct |
| OTIS Mock AIME | 5.1% | 8.5% | Mistral Large 2407 |
| MATH level 5 | 41.6% | 44.8% | Mistral Large 2407 |
| LMArena Text: math | 1267 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: mathematical work | 1267 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Llama 3.3 70B Instruct | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.3% | 68.3% | Llama 3.3 70B Instruct |
| MMLU | 86.3% | 80.0% | Llama 3.3 70B Instruct |
| LMArena Text: science | 1284 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: medicine and healthcare | 1275 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: legal and government | 1284 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: business and finance | 1243 | Not measured | Only Llama 3.3 70B Instruct measured |
| 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 | Mistral Large 2407 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index21
| Benchmark | Llama 3.3 70B Instruct | Mistral Large 2407 | Leader |
|---|---|---|---|
| IFBench | 47.1% | 31.6% | Llama 3.3 70B Instruct |
| Long context reasoning (AA-LCR) | 15.7% | 2.0% | Llama 3.3 70B Instruct |
| DTBench | 59.5% | 61.2% | Mistral Large 2407 |
| 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 |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| LMArena Text | 1274 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: instruction following | 1242 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: creative writing | 1250 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: English | 1303 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: non-English | 1236 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Chinese | 1217 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: German | 1251 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Japanese | 1150 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Korean | 1142 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: French | 1282 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Spanish | 1269 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Russian | 1252 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: Polish | 1259 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: multi-turn | 1280 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: longer queries | 1255 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: writing and language | 1255 | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: entertainment and media | 1241 | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 23 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 2407 | 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 2407 |
|---|---|---|
| Maker | Meta Llama | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | December 6, 2024 | November 19, 2024 |
| Context window | 131K | 131K |
| 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 2407 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 8.3x cheaper ($0.72 vs $6.00 per million) and work measured by τ²-Bench Telecom or GPQA Diamond (Epoch AI run), where it leads.
Which is cheaper, Llama 3.3 70B Instruct or Mistral Large 2407?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 per million output tokens; Mistral Large 2407 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 2407?
Neither: both take 131K tokens.
How many benchmarks is the Llama 3.3 70B Instruct vs Mistral Large 2407 comparison based on?
16 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 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.