Model profile
MiniMax

MiniMax: MiniMax M2

MiniMax: MiniMax M2 is a budget-priced text-first model from MiniMax with balanced runtime profile, extended context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsBalanced latencyExtended contextBudget pricing
Intelligence
36.1

Benchmark blend

Coding
29.2

Dev workflow signal

Context
197K Tokens

Extended

Input Price
$0.30

Budget tier

Decision snapshot
55

MiniMax: MiniMax M2 currently reads as a budget text-first option with extended context and a balanced runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Agent workflows
Latency tier
Balanced
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
36.1
36

General reasoning and benchmark headroom.

Limited
Speed
75 tok/s
65

TTFT 1.17s

Competitive
Context
197K Tokens
76

How much prompt and task state can stay in view.

Competitive
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 29Math 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 / Agent workflows with extended context and balanced runtime.

Cost posture

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

Strengths
  • 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 is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

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

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

Explore Next

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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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MiniMax

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Intelligence
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Context
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Input Price
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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
36.1
MMLU Pro
82.0%
GPQA
77.7%
HLE
12.5%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
29.2
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
61.0%

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
197K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Other
Max Completion
196608
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biasmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_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.

OR Cache Read
$0.00

Metadata

Raw source tables at the end

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