Model profile
MiniMax

MiniMax: MiniMax M2

MiniMax: MiniMax M2 is a budget-priced text-first model from MiniMax with partial runtime data, large context posture, and the clearest fit around long-context research / coding.

Best for: Long-context research / CodingN/A latencyLarge contextBudget pricing
Intelligence
28.9

Benchmark blend

Coding
0.826

Dev workflow signal

Context
205K Tokens

Large

Input Price
$0.30

Budget tier

Decision snapshot
62

MiniMax: MiniMax M2 currently reads as a budget text-first option with large context and a partially published runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Coding
Latency tier
N/A
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
28.9
29

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
205K Tokens
88

How much prompt and task state can stay in view.

Above average
Price
$0.30
86

$1.20 output / 1M

Efficient

Editorial Profile

MiniMax: MiniMax M2 in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Selective fitCoding score 83Math score 78

MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning,...

Identity

MiniMax text-first profile

Positioning

Long-context research / Coding with large context and partially published runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Coding indicators point to a strong developer workflow fit.

  • Large context headroom supports repo-wide prompts and long research sessions.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit
  • Code generation, refactors, test writing, and tool-assisted debugging.

  • Long-context summarization, repo analysis, and policy or document review.

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Context
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Input Price
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Intelligence
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Context
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Input Price
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Intelligence
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Context
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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
28.9
MMLU Pro
82.0%
GPQA
77.7%
HLE
13.7%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.826
SciCode
36.1%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
78.3
AIME 2025
78.3%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
72.3%
TAU2
86.8%
TerminalBench Hard
25.8%
LCR
65.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
205K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Other
Max Completion
131072
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoninglogprobsmax_tokenspresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.30
Output
per 1M output tokens
$1.20
Blended
AA 3:1 mix
$0.53

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.