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Llama 3.1 8B Instruct vs MiniMax M2.7

MiniMax M2.7 leads Llama 3.1 8B Instruct on the Artificial Analysis Intelligence Index, 22.8 to 6.9, and Llama 3.1 8B Instruct costs 15.0x less per output token ($0.08 vs $1.20 per million). Updated October 22, 2026; 37 benchmarks have a result for both.

Replace Llama 3.1 8B Instruct Replace MiniMax M2.7
Llama 3.1 8B InstructMeta Llama
Coding
No data
Overall
17.9
Output $/M
$0.08
0
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
37
vs boards won of 37 shared

Llama 3.1 8B Instruct or MiniMax M2.7: which to pick

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; Llama 3.1 8B Instruct has none, so the two cannot be ranked on coding.

Pick Llama 3.1 8B Instruct for output tokens 15.0x cheaper ($0.08 vs $1.20 per million).

Model ALlama 3.1 8B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
37
Llama 3.1 8B Instruct only
14
MiniMax M2.7 only
37
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.

At a glance

Coding index
Llama 3.1 8B Instruct not benchmarked, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Agentic work, 68.4 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Llama 3.1 8B Instruct 16.4% against MiniMax M2.7 84.8%, 68.4 points apart on a 0 to 100 scale.
Output price
Llama 3.1 8B Instruct $0.08 against MiniMax M2.7 $1.20 per million output tokens, 15.0x apart.
Context window
Llama 3.1 8B Instruct takes 131K tokens, MiniMax M2.7 205K.

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
CategoryLlama 3.1 8B InstructBarsMiniMax M2.7Ties
Coding05
Intelligence02
Agentic work01
Reasoning05
Math02
Knowledge04
General018
All shared boards037
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
Coding benchmarks for Llama 3.1 8B Instruct and MiniMax M2.7
BenchmarkLlama 3.1 8B InstructMiniMax M2.7Leader
LMArena WebDevNot measured1399Only MiniMax M2.7 measured
AA Coding Index5.452.6MiniMax M2.7
LiveCodeBench (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
SWE-bench Verified (Vals AI)Not measured73.8%Only MiniMax M2.7 measured
Vibe Code Bench (Vals AI)Not measured11.9%Only MiniMax M2.7 measured
Code migration (Vals AI)Not measured8.7%Only MiniMax M2.7 measured
ProgramBench (Vals AI)Not measured0.0%Only MiniMax M2.7 measured
Terminal-Bench 2.1 (Vals AI)Not measured48.7%Only MiniMax M2.7 measured
LiveCodeBench (Artificial Analysis run)11.6%Not measuredOnly Llama 3.1 8B Instruct measured
SciCodeNot measured50.1%Only MiniMax M2.7 measured
Terminal-Bench Hard (Artificial Analysis run)0.8%39.4%MiniMax M2.7
Terminal-BenchNot measured45.1%Only MiniMax M2.7 measured
WeirdML1.7%37.0%MiniMax M2.7
ALE-BenchNot measured599.3Only MiniMax M2.7 measured
LMArena Text: coding11951454MiniMax M2.7
LMArena Text: software and IT11951446MiniMax M2.7
LMArena WebDev: ReactNot measured1388Only MiniMax M2.7 measured
LMArena WebDev: HTMLNot measured1401Only MiniMax M2.7 measured
Neither model has a result on the other 21 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Llama 3.1 8B Instruct and MiniMax M2.7
BenchmarkLlama 3.1 8B InstructMiniMax M2.7Leader
AA Intelligence Index6.922.8MiniMax M2.7
Epoch Capabilities Index116.5145.8MiniMax M2.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Llama 3.1 8B Instruct and MiniMax M2.7
BenchmarkLlama 3.1 8B InstructMiniMax M2.7Leader
AA Agentic IndexNot measured15.3Only MiniMax M2.7 measured
Finance Agent (Vals AI)Not measured27.9%Only MiniMax M2.7 measured
Tax Agent Bench (Vals AI)Not measured6.9%Only MiniMax M2.7 measured
Legal Research Bench (Vals AI)Not measured10.6%Only MiniMax M2.7 measured
Excel Modeling Benchmark (Vals AI)Not measured28.4%Only MiniMax M2.7 measured
τ²-Bench Telecom16.4%84.8%MiniMax M2.7
BALROG15.1%Not measuredOnly Llama 3.1 8B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)25.8%Not measuredOnly Llama 3.1 8B Instruct measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Llama 3.1 8B Instruct and MiniMax M2.7
BenchmarkLlama 3.1 8B InstructMiniMax M2.7Leader
GPQA DiamondNot measured86.6%Only MiniMax M2.7 measured
GPQA Diamond (Artificial Analysis run)25.9%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)5.3%29.6%MiniMax M2.7
GPQA Diamond (Epoch AI run)27.0%Not measuredOnly Llama 3.1 8B Instruct measured
LMArena Text: hard prompts11751422MiniMax M2.7
LMArena Text: expert11441443MiniMax M2.7
LMArena Text: hard prompts in English11961441MiniMax M2.7
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Llama 3.1 8B Instruct and MiniMax M2.7
BenchmarkLlama 3.1 8B InstructMiniMax M2.7Leader
AIMENot measured91.0%Only MiniMax M2.7 measured
AA Math Index4.3Not measuredOnly Llama 3.1 8B Instruct measured
AIME 2025 (Artificial Analysis run)4.3%Not measuredOnly Llama 3.1 8B Instruct measured
OTIS Mock AIME1.7%Not measuredOnly Llama 3.1 8B Instruct measured
ProofBenchNot measured3.0%Only MiniMax M2.7 measured
MATH level 522.9%Not measuredOnly Llama 3.1 8B Instruct measured
GSM8K82.4%Not measuredOnly Llama 3.1 8B Instruct measured
LMArena Text: math11791421MiniMax M2.7
LMArena Text: mathematical work11801437MiniMax M2.7
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Llama 3.1 8B Instruct and MiniMax M2.7
BenchmarkLlama 3.1 8B InstructMiniMax M2.7Leader
MMLU ProNot measured80.4%Only MiniMax M2.7 measured
LegalBench (Vals AI)Not measured84.0%Only MiniMax M2.7 measured
CaseLaw (Vals AI)Not measured60.9%Only MiniMax M2.7 measured
CorpFin (Vals AI)Not measured61.2%Only MiniMax M2.7 measured
TaxEval (Vals AI)Not measured66.6%Only MiniMax M2.7 measured
MedCode (Vals AI)Not measured34.4%Only MiniMax M2.7 measured
MMLU Pro (Artificial Analysis run)47.6%Not measuredOnly Llama 3.1 8B Instruct measured
MMLU56.1%Not measuredOnly Llama 3.1 8B Instruct measured
LMArena Text: science11931416MiniMax M2.7
LMArena Text: medicine and healthcare11821409MiniMax M2.7
LMArena Text: legal and government12061403MiniMax M2.7
LMArena Text: business and finance11661407MiniMax M2.7
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Llama 3.1 8B Instruct and MiniMax M2.7
BenchmarkLlama 3.1 8B InstructMiniMax M2.7Leader
Design Arena: websitesNot measured1243Only MiniMax M2.7 measured
Design Arena: UI componentsNot measured1210Only MiniMax M2.7 measured
Design Arena: game devNot measured1209Only MiniMax M2.7 measured
Design Arena: data vizNot measured1233Only MiniMax M2.7 measured
Design Arena: 3DNot measured1195Only MiniMax M2.7 measured
Design Arena: all code categoriesNot measured1233Only MiniMax M2.7 measured
Design Arena: SVGNot measured1137Only MiniMax M2.7 measured
Design Arena: ASCII artNot measured1136Only MiniMax M2.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for Llama 3.1 8B Instruct and MiniMax M2.7
BenchmarkLlama 3.1 8B InstructMiniMax M2.7Leader
MedScribe (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
IFBench28.6%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)18.0%78.3%MiniMax M2.7
DTBench50.9%Not measuredOnly Llama 3.1 8B Instruct measured
LMCA5.4%Not measuredOnly Llama 3.1 8B Instruct measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
PIQA81.2%Not measuredOnly Llama 3.1 8B Instruct measured
LMArena Text11871405MiniMax M2.7
LMArena Text: instruction following11591405MiniMax M2.7
LMArena Text: creative writing11541354MiniMax M2.7
LMArena Text: English12181426MiniMax M2.7
LMArena Text: non-English11481382MiniMax M2.7
LMArena Text: Chinese11511442MiniMax M2.7
LMArena Text: German11441398MiniMax M2.7
LMArena Text: Japanese10611260MiniMax M2.7
LMArena Text: Korean10531314MiniMax M2.7
LMArena Text: French11781424MiniMax M2.7
LMArena Text: Spanish11691402MiniMax M2.7
LMArena Text: Russian11581383MiniMax M2.7
LMArena Text: PolishNot measured1387Only MiniMax M2.7 measured
LMArena Text: multi-turn11721412MiniMax M2.7
LMArena Text: longer queries11811418MiniMax M2.7
LMArena Text: writing and language11621376MiniMax M2.7
LMArena Text: entertainment and media11451360MiniMax M2.7
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Llama 3.1 8B Instruct and MiniMax M2.7
BenchmarkLlama 3.1 8B InstructMiniMax M2.7Leader
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 3.1 8B Instruct$0.0001Fits in one request
MiniMax M2.7$0.0009Fits in one request

Llama 3.1 8B Instruct costs less

Repository review

50K input + 3K output tokens

Llama 3.1 8B Instruct$0.0027Fits in one request
MiniMax M2.7$0.019Fits in one request

Llama 3.1 8B Instruct costs less

Agent loop

200K input + 10K output tokens

Llama 3.1 8B Instruct$0.011Exceeds the context window
MiniMax M2.7$0.072Exceeds the context window

Llama 3.1 8B 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.

Specifications of Llama 3.1 8B Instruct and MiniMax M2.7
SpecificationMeta Llama 3.1 8B InstructMiniMax M2.7
MakerMeta LlamaMiniMax
WeightsOpen weightsOpen weights
ReleasedJuly 23, 2024March 18, 2026
Context window131K205K
Input $/Mtok$0.05$0.30
Output $/Mtok$0.08$1.20
Coding indexNot benchmarked36.2
Independent measurements behind it03
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is Llama 3.1 8B Instruct better than MiniMax M2.7 for coding?

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; Llama 3.1 8B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.1 8B Instruct for output tokens 15.0x cheaper ($0.08 vs $1.20 per million).

Which is cheaper, Llama 3.1 8B Instruct or MiniMax M2.7?

Llama 3.1 8B Instruct costs $0.05 per million input tokens and $0.08 per million output tokens; MiniMax M2.7 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.7 only.

Which has the bigger context window, Llama 3.1 8B Instruct or MiniMax M2.7?

MiniMax M2.7, with 205K tokens against 131K.

How many benchmarks is the Llama 3.1 8B Instruct vs MiniMax M2.7 comparison based on?

37 benchmarks have a result for both models as of October 22, 2026. Llama 3.1 8B Instruct's coding index rests on 0 independent benchmarks and MiniMax M2.7's on 3. Where only one model has a result, the page says so instead of naming a winner.

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