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Phi 4 vs Mixtral 8x22B Instruct

Updated October 21, 2026. 6 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Phi 4Microsoft
Coding
No data
Overall
5.9
Output $/M
$0.14
5
Mixtral 8x22B InstructMistral AI
Coding
No data
Overall
5.7
Output $/M
$6.00
1
vs boards won of 6 shared

Decision reading

Neither model has a coding score yet, so the two cannot be ranked on coding.

Phi 4 has the edge where output tokens cost 42.9x less, and where the benchmark that matters is Humanity's Last Exam (Artificial Analysis run), where it leads.

Shared results
6
Phi 4 only
39
Mixtral 8x22B Instruct only
1
Like-for-like categories
4 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.

At a glance

Coding index
Phi 4 not benchmarked, Mixtral 8x22B Instruct not benchmarked.
Widest category
Knowledge, 17.7 points apart on average over 1 shared benchmark.
Widest single gap
GPQA Diamond (Artificial Analysis run): Phi 4 57.5% against Mixtral 8x22B Instruct 33.2%, 24.3 points apart on a 0 to 100 scale.
Output price
Phi 4 $0.14 against Mixtral 8x22B Instruct $6.00 per million output tokens, 42.9x apart.
Context window
Phi 4 takes 16K tokens, Mixtral 8x22B Instruct 66K.

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.

Boards won, where both have a result
CategoryPhi 4BarsMixtral 8x22B InstructTies
Coding10
Intelligence20
Reasoning11
Knowledge10
All shared boards51
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
Coding benchmarks for Phi 4 and Mixtral 8x22B Instruct
BenchmarkPhi 4Mixtral 8x22B InstructLeader
LiveCodeBench (Artificial Analysis run)23.1%14.8%Phi 4
Terminal-Bench Hard (Artificial Analysis run)3.8%Not measuredOnly Phi 4 measured
LMArena Text: coding1231Not measuredOnly Phi 4 measured
LMArena Text: software and IT1228Not measuredOnly Phi 4 measured
Neither model has a result on the other 34 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Phi 4 and Mixtral 8x22B Instruct
BenchmarkPhi 4Mixtral 8x22B InstructLeader
AA Intelligence Index5.95.7Phi 4
Epoch Capabilities Index130.5122.0Phi 4
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Agentic work benchmarks for Phi 4 and Mixtral 8x22B Instruct
BenchmarkPhi 4Mixtral 8x22B InstructLeader
τ²-Bench Telecom0.0%Not measuredOnly Phi 4 measured
BALROG11.6%Not measuredOnly Phi 4 measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index11
Reasoning benchmarks for Phi 4 and Mixtral 8x22B Instruct
BenchmarkPhi 4Mixtral 8x22B InstructLeader
GPQA Diamond (Artificial Analysis run)57.5%33.2%Phi 4
Humanity's Last Exam (Artificial Analysis run)3.8%4.0%Mixtral 8x22B Instruct
GPQA Diamond (Epoch AI run)56.1%Not measuredOnly Phi 4 measured
LMArena Text: hard prompts1220Not measuredOnly Phi 4 measured
LMArena Text: expert1203Not measuredOnly Phi 4 measured
LMArena Text: hard prompts in English1230Not measuredOnly Phi 4 measured
Neither model has a result on the other 15 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for Phi 4 and Mixtral 8x22B Instruct
BenchmarkPhi 4Mixtral 8x22B InstructLeader
AA Math Index18.0Not measuredOnly Phi 4 measured
AIME 2025 (Artificial Analysis run)18.0%Not measuredOnly Phi 4 measured
OTIS Mock AIME13.8%Not measuredOnly Phi 4 measured
MATH level 564.9%Not measuredOnly Phi 4 measured
LMArena Text: math1246Not measuredOnly Phi 4 measured
LMArena Text: mathematical work1254Not measuredOnly 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 index10
Knowledge benchmarks for Phi 4 and Mixtral 8x22B Instruct
BenchmarkPhi 4Mixtral 8x22B InstructLeader
MMLU Pro (Artificial Analysis run)71.4%53.7%Phi 4
MMLU84.8%Not measuredOnly Phi 4 measured
LMArena Text: science1206Not measuredOnly Phi 4 measured
LMArena Text: medicine and healthcare1202Not measuredOnly Phi 4 measured
LMArena Text: legal and government1241Not measuredOnly Phi 4 measured
LMArena Text: business and finance1199Not measuredOnly Phi 4 measured
Neither model has a result on the other 11 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Phi 4 and Mixtral 8x22B Instruct
BenchmarkPhi 4Mixtral 8x22B InstructLeader
Neither model has a result on 9 boards here
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards, none shared
General benchmarks for Phi 4 and Mixtral 8x22B Instruct
BenchmarkPhi 4Mixtral 8x22B InstructLeader
IFBench23.5%Not measuredOnly Phi 4 measured
Long context reasoning (AA-LCR)0.0%Not measuredOnly Phi 4 measured
DTBenchNot measured55.1%Only Mixtral 8x22B Instruct measured
Creative writing (Lech Mazur)62.6%Not measuredOnly Phi 4 measured
LMArena Text1217Not measuredOnly Phi 4 measured
LMArena Text: instruction following1201Not measuredOnly Phi 4 measured
LMArena Text: creative writing1181Not measuredOnly Phi 4 measured
LMArena Text: English1231Not measuredOnly Phi 4 measured
LMArena Text: non-English1197Not measuredOnly Phi 4 measured
LMArena Text: Chinese1212Not measuredOnly Phi 4 measured
LMArena Text: German1222Not measuredOnly Phi 4 measured
LMArena Text: Japanese1158Not measuredOnly Phi 4 measured
LMArena Text: Korean1151Not measuredOnly Phi 4 measured
LMArena Text: French1224Not measuredOnly Phi 4 measured
LMArena Text: Spanish1234Not measuredOnly Phi 4 measured
LMArena Text: Russian1209Not measuredOnly Phi 4 measured
LMArena Text: multi-turn1206Not measuredOnly Phi 4 measured
LMArena Text: longer queries1217Not measuredOnly Phi 4 measured
LMArena Text: writing and language1198Not measuredOnly Phi 4 measured
LMArena Text: entertainment and media1162Not measuredOnly 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
Image generation benchmarks for Phi 4 and Mixtral 8x22B Instruct
BenchmarkPhi 4Mixtral 8x22B InstructLeader
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$0.0001Fits in one request
Mixtral 8x22B Instruct$0.0050Fits in one request

Phi 4 costs less

Repository review

50K input + 3K output tokens

Phi 4$0.0039Exceeds the context window
Mixtral 8x22B Instruct$0.118Fits in one request

Phi 4 costs less

Agent loop

200K input + 10K output tokens

Phi 4$0.015Exceeds the context window
Mixtral 8x22B Instruct$0.460Exceeds the context window

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.

Specifications of Phi 4 and Mixtral 8x22B Instruct
SpecificationMicrosoft Phi 4Mistral Mixtral 8x22B Instruct
MakerMicrosoftMistral AI
WeightsOpen weightsOpen weights
ReleasedJanuary 10, 2025April 17, 2024
Context window16K66K
Input $/Mtok$0.07$2.00
Output $/Mtok$0.14$6.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Phi 4 better than Mixtral 8x22B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Phi 4 has the edge where output tokens cost 42.9x less, and where the benchmark that matters is Humanity's Last Exam (Artificial Analysis run), where it leads.

Which is cheaper, Phi 4 or Mixtral 8x22B Instruct?

Phi 4 costs $0.07 per million input tokens and $0.14 per million output tokens; Mixtral 8x22B Instruct 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 Mixtral 8x22B Instruct?

Mixtral 8x22B Instruct, with 66K tokens against 16K.

How many benchmarks is the Phi 4 vs Mixtral 8x22B Instruct comparison based on?

6 benchmarks have a result for both models as of October 21, 2026. Phi 4's coding index rests on 0 independent benchmarks and Mixtral 8x22B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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