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Llama 3.3 70B Instruct vs MiniMax M2.7

MiniMax M2.7 leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 22.8 to 7.7, and Llama 3.3 70B Instruct costs 1.7x less per output token ($0.72 vs $1.20 per million). Updated October 22, 2026; 38 benchmarks have a result for both.

Replace Llama 3.3 70B Instruct Replace MiniMax M2.7
Llama 3.3 70B InstructMeta Llama
Coding
No data
Overall
26.0
Output $/M
$0.72
0
MiniMax M2.7MiniMax
Coding
36.2
Overall
44.5
Output $/M
$1.20
38
vs boards won of 38 shared

Llama 3.3 70B Instruct or MiniMax M2.7: which to pick

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding.

Pick Llama 3.3 70B Instruct for output tokens 1.7x cheaper ($0.72 vs $1.20 per million).

Model ALlama 3.3 70B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved taskModel BMiniMax M2.7MiniMax36.2/1003 independent benchmarks$0.64 per solved task
Shared results
38
Llama 3.3 70B Instruct only
14
MiniMax M2.7 only
36
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 3.3 70B Instruct not benchmarked, MiniMax M2.7 36.2 from 3 benchmarks.
Widest category
Agentic work, 58.2 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): Llama 3.3 70B Instruct 15.7% against MiniMax M2.7 78.3%, 62.7 points apart on a 0 to 100 scale.
Output price
Llama 3.3 70B Instruct $0.72 against MiniMax M2.7 $1.20 per million output tokens, 1.7x apart.
Context window
Llama 3.3 70B 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 3.3 70B InstructBarsMiniMax M2.7Ties
Coding05
Intelligence02
Agentic work01
Reasoning05
Math02
Knowledge04
General019
All shared boards038
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
Coding benchmarks for Llama 3.3 70B Instruct and MiniMax M2.7
BenchmarkLlama 3.3 70B InstructMiniMax M2.7Leader
LMArena WebDevNot measured1399Only MiniMax M2.7 measured
AA Coding Index11.952.6MiniMax 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)28.8%Not measuredOnly Llama 3.3 70B Instruct measured
SciCodeNot measured50.1%Only MiniMax M2.7 measured
Terminal-Bench Hard (Artificial Analysis run)3.0%39.4%MiniMax M2.7
Terminal-BenchNot measured45.1%Only MiniMax M2.7 measured
WeirdML14.4%37.0%MiniMax M2.7
ALE-BenchNot measured599.3Only MiniMax M2.7 measured
LMArena Text: coding12681454MiniMax M2.7
LMArena Text: software and IT12731446MiniMax 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 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Llama 3.3 70B Instruct and MiniMax M2.7
BenchmarkLlama 3.3 70B InstructMiniMax M2.7Leader
AA Intelligence Index7.722.8MiniMax M2.7
Epoch Capabilities Index127.3145.8MiniMax M2.7
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Llama 3.3 70B Instruct and MiniMax M2.7
BenchmarkLlama 3.3 70B 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 Telecom26.6%84.8%MiniMax M2.7
BALROG23.0%Not measuredOnly Llama 3.3 70B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)31.9%Not measuredOnly Llama 3.3 70B Instruct measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Llama 3.3 70B Instruct and MiniMax M2.7
BenchmarkLlama 3.3 70B InstructMiniMax M2.7Leader
GPQA DiamondNot measured86.6%Only MiniMax M2.7 measured
GPQA Diamond (Artificial Analysis run)49.8%87.4%MiniMax M2.7
Humanity's Last Exam (Artificial Analysis run)3.6%29.6%MiniMax M2.7
GPQA Diamond (Epoch AI run)47.4%Not measuredOnly Llama 3.3 70B Instruct measured
SimpleBench19.9%Not measuredOnly Llama 3.3 70B Instruct measured
LMArena Text: hard prompts12571422MiniMax M2.7
LMArena Text: expert12251443MiniMax M2.7
LMArena Text: hard prompts in English12771441MiniMax 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 3.3 70B Instruct and MiniMax M2.7
BenchmarkLlama 3.3 70B InstructMiniMax M2.7Leader
AIMENot measured91.0%Only MiniMax M2.7 measured
AA Math Index7.7Not measuredOnly Llama 3.3 70B Instruct measured
AIME 2025 (Artificial Analysis run)7.7%Not measuredOnly Llama 3.3 70B Instruct measured
OTIS Mock AIME5.1%Not measuredOnly Llama 3.3 70B Instruct measured
ProofBenchNot measured3.0%Only MiniMax M2.7 measured
MATH level 541.6%Not measuredOnly Llama 3.3 70B Instruct measured
LMArena Text: math12671421MiniMax M2.7
LMArena Text: mathematical work12671437MiniMax M2.7
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Llama 3.3 70B Instruct and MiniMax M2.7
BenchmarkLlama 3.3 70B 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)71.3%Not measuredOnly Llama 3.3 70B Instruct measured
MMLU86.3%Not measuredOnly Llama 3.3 70B Instruct measured
LMArena Text: science12841416MiniMax M2.7
LMArena Text: medicine and healthcare12751409MiniMax M2.7
LMArena Text: legal and government12841403MiniMax M2.7
LMArena Text: business and finance12431407MiniMax M2.7
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Llama 3.3 70B Instruct and MiniMax M2.7
BenchmarkLlama 3.3 70B InstructMiniMax M2.7Leader
Design Arena: websitesNot measured1242Only MiniMax M2.7 measured
Design Arena: UI componentsNot measured1207Only 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 measured1194Only 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 index019
General benchmarks for Llama 3.3 70B Instruct and MiniMax M2.7
BenchmarkLlama 3.3 70B InstructMiniMax M2.7Leader
MedScribe (Vals AI)Not measured79.9%Only MiniMax M2.7 measured
IFBench47.1%75.7%MiniMax M2.7
Long context reasoning (AA-LCR)15.7%78.3%MiniMax M2.7
DTBench59.5%Not measuredOnly Llama 3.3 70B Instruct measured
LMCA17.5%Not measuredOnly Llama 3.3 70B Instruct measured
Fiction.LiveBench33.3%Not measuredOnly Llama 3.3 70B Instruct measured
ExploitBenchNot measured13.3%Only MiniMax M2.7 measured
LMArena Text12741405MiniMax M2.7
LMArena Text: instruction following12421405MiniMax M2.7
LMArena Text: creative writing12501354MiniMax M2.7
LMArena Text: English13031426MiniMax M2.7
LMArena Text: non-English12361382MiniMax M2.7
LMArena Text: Chinese12171442MiniMax M2.7
LMArena Text: German12511398MiniMax M2.7
LMArena Text: Japanese11501260MiniMax M2.7
LMArena Text: Korean11421314MiniMax M2.7
LMArena Text: French12821424MiniMax M2.7
LMArena Text: Spanish12691402MiniMax M2.7
LMArena Text: Russian12521383MiniMax M2.7
LMArena Text: Polish12591387MiniMax M2.7
LMArena Text: multi-turn12801412MiniMax M2.7
LMArena Text: longer queries12551418MiniMax M2.7
LMArena Text: writing and language12551376MiniMax M2.7
LMArena Text: entertainment and media12411360MiniMax M2.7
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
Image generation benchmarks for Llama 3.3 70B Instruct and MiniMax M2.7
BenchmarkLlama 3.3 70B 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 3.3 70B Instruct$0.0008Fits in one request
MiniMax M2.7$0.0009Fits in one request

Llama 3.3 70B Instruct costs less

Repository review

50K input + 3K output tokens

Llama 3.3 70B Instruct$0.025Fits in one request
MiniMax M2.7$0.019Fits in one request

MiniMax M2.7 costs less

Agent loop

200K input + 10K output tokens

Llama 3.3 70B Instruct$0.097Exceeds the context window
MiniMax M2.7$0.072Exceeds the context window

MiniMax M2.7 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 3.3 70B Instruct and MiniMax M2.7
SpecificationMeta Llama 3.3 70B InstructMiniMax M2.7
MakerMeta LlamaMiniMax
WeightsOpen weightsOpen weights
ReleasedDecember 6, 2024March 18, 2026
Context window131K205K
Input $/Mtok$0.45$0.30
Output $/Mtok$0.72$1.20
Coding indexNot benchmarked36.2
Independent measurements behind it03
Cost per solved taskNot computable$0.64
SourceOpenRouter listingOpenRouter listing

Questions

Is Llama 3.3 70B Instruct better than MiniMax M2.7 for coding?

Only MiniMax M2.7 has a coding score, 36.2 on the Vibe Coding Index; Llama 3.3 70B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.3 70B Instruct for output tokens 1.7x cheaper ($0.72 vs $1.20 per million).

Which is cheaper, Llama 3.3 70B Instruct or MiniMax M2.7?

Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 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 3.3 70B Instruct or MiniMax M2.7?

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

How many benchmarks is the Llama 3.3 70B Instruct vs MiniMax M2.7 comparison based on?

38 benchmarks have a result for both models as of October 22, 2026. Llama 3.3 70B 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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