Phi 4 vs Mistral Large 2407
Mistral Large 2407 leads Phi 4 on the Artificial Analysis Intelligence Index, 7.6 to 5.9, and Phi 4 costs 42.9x less per output token ($0.14 vs $6.00 per million). Updated October 21, 2026; 15 benchmarks have a result for both.
Phi 4 or Mistral Large 2407: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Phi 4 for output tokens 42.9x cheaper ($0.14 vs $6.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 15
- Phi 4 only
- 31
- Mistral Large 2407 only
- 2
- 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 Large 2407
Mistral Large 2407 leads, 7.6 against 5.9.
Same board, both measured
Long documents
Listed context window
Mistral Large 2407
Mistral Large 2407 takes 131K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Phi 4
Phi 4 $0.0001, Mistral Large 2407 $0.0050 per turn.
List prices
At a glance
- Coding index
- Phi 4 not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- Agentic work, 33.0 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Phi 4 0.0% against Mistral Large 2407 33.0%, 33.0 points apart on a 0 to 100 scale.
- Output price
- Phi 4 $0.14 against Mistral Large 2407 $6.00 per million output tokens, 42.9x apart.
- Context window
- Phi 4 takes 16K tokens, Mistral Large 2407 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 | Phi 4 | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 3 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 2 | 0 | ||
| General | 0 | 3 | ||
| All shared boards | 9 | 6 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Phi 4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 23.1% | 26.7% | Mistral Large 2407 |
| Terminal-Bench Hard (Artificial Analysis run) | 3.8% | Not measured | Only Phi 4 measured |
| LMArena Text: coding | 1231 | Not measured | Only Phi 4 measured |
| LMArena Text: software and IT | 1228 | Not measured | Only Phi 4 measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Phi 4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.9 | 7.6 | Mistral Large 2407 |
| Epoch Capabilities Index | 130.5 | Not measured | Only Phi 4 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Phi 4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 0.0% | 33.0% | Mistral Large 2407 |
| BALROG | 11.6% | Not measured | Only Phi 4 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 28.8% | Not measured | Only Phi 4 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Phi 4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 57.5% | 47.2% | Phi 4 |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 2.9% | Phi 4 |
| GPQA Diamond (Epoch AI run) | 56.1% | 49.0% | Phi 4 |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 measured |
| LMArena Text: hard prompts | 1220 | Not measured | Only Phi 4 measured |
| LMArena Text: expert | 1203 | Not measured | Only Phi 4 measured |
| LMArena Text: hard prompts in English | 1230 | Not measured | Only Phi 4 measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | Phi 4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Math Index | 18.0 | 0.0 | Phi 4 |
| AIME 2025 (Artificial Analysis run) | 18.0% | 0.0% | Phi 4 |
| OTIS Mock AIME | 13.8% | 8.5% | Phi 4 |
| MATH level 5 | 64.9% | 44.8% | Phi 4 |
| LMArena Text: math | 1246 | Not measured | Only Phi 4 measured |
| LMArena Text: mathematical work | 1254 | Not measured | Only Phi 4 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 | Phi 4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.4% | 68.3% | Phi 4 |
| MMLU | 84.8% | 80.0% | Phi 4 |
| LMArena Text: science | 1206 | Not measured | Only Phi 4 measured |
| LMArena Text: medicine and healthcare | 1202 | Not measured | Only Phi 4 measured |
| LMArena Text: legal and government | 1241 | Not measured | Only Phi 4 measured |
| LMArena Text: business and finance | 1199 | Not measured | Only Phi 4 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 | Phi 4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Phi 4 | Mistral Large 2407 | Leader |
|---|---|---|---|
| IFBench | 23.5% | 31.6% | Mistral Large 2407 |
| Long context reasoning (AA-LCR) | 0.0% | 2.0% | Mistral Large 2407 |
| DTBench | Not measured | 61.2% | Only Mistral Large 2407 measured |
| Creative writing (Lech Mazur) | 62.6% | 69.0% | Mistral Large 2407 |
| LMArena Text | 1217 | Not measured | Only Phi 4 measured |
| LMArena Text: instruction following | 1201 | Not measured | Only Phi 4 measured |
| LMArena Text: creative writing | 1181 | Not measured | Only Phi 4 measured |
| LMArena Text: English | 1231 | Not measured | Only Phi 4 measured |
| LMArena Text: non-English | 1197 | Not measured | Only Phi 4 measured |
| LMArena Text: Chinese | 1212 | Not measured | Only Phi 4 measured |
| LMArena Text: German | 1222 | Not measured | Only Phi 4 measured |
| LMArena Text: Japanese | 1158 | Not measured | Only Phi 4 measured |
| LMArena Text: Korean | 1151 | Not measured | Only Phi 4 measured |
| LMArena Text: French | 1224 | Not measured | Only Phi 4 measured |
| LMArena Text: Spanish | 1234 | Not measured | Only Phi 4 measured |
| LMArena Text: Russian | 1209 | Not measured | Only Phi 4 measured |
| LMArena Text: multi-turn | 1206 | Not measured | Only Phi 4 measured |
| LMArena Text: longer queries | 1217 | Not measured | Only Phi 4 measured |
| LMArena Text: writing and language | 1198 | Not measured | Only Phi 4 measured |
| LMArena Text: entertainment and media | 1162 | Not measured | Only Phi 4 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 | Phi 4 | 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
Phi 4 costs less
Repository review
50K input + 3K output tokens
Phi 4 costs less
Agent loop
200K input + 10K output tokens
Phi 4 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 | Microsoft Phi 4 | Mistral Large 2407 |
|---|---|---|
| Maker | Microsoft | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | January 10, 2025 | November 19, 2024 |
| Context window | 16K | 131K |
| Input $/Mtok | $0.07 | $2.00 |
| Output $/Mtok | $0.14 | $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 Phi 4 better than Mistral Large 2407 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Phi 4 for output tokens 42.9x cheaper ($0.14 vs $6.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or AA Intelligence Index, where it leads.
Which is cheaper, Phi 4 or Mistral Large 2407?
Phi 4 costs $0.07 per million input tokens and $0.14 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, Phi 4 or Mistral Large 2407?
Mistral Large 2407, with 131K tokens against 16K.
How many benchmarks is the Phi 4 vs Mistral Large 2407 comparison based on?
15 benchmarks have a result for both models as of October 21, 2026. Phi 4'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.