Model profile
MiniMax

MiniMax: MiniMax M2.1

MiniMax: MiniMax M2.1 is a budget-priced text-first model from MiniMax with fast runtime profile, extended context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsFast latencyExtended contextBudget pricing
Intelligence
39.4

Benchmark blend

Coding
32.8

Dev workflow signal

Context
197K Tokens

Extended

Input Price
$0.30

Budget tier

Decision snapshot
58

MiniMax: MiniMax M2.1 currently reads as a budget text-first option with extended context and a fast runtime profile.

Overall profile
Selective fit
Best for
Long-context research / Agent workflows
Latency tier
Fast
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
39.4
39

General reasoning and benchmark headroom.

Limited
Speed
100 tok/s
74

TTFT 1.19s

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 M2.1 in one narrative

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

Selective fitCoding score 33Math score 83

MiniMax-M2.1 is a lightweight, state-of-the-art large language model optimized for coding, agentic workflows, and modern application development. With only 10 billion activated parameters, it delivers a major jump in real-world...

Identity

MiniMax text-first profile

Positioning

Long-context research / Agent workflows with extended context and fast 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.

  • Latency and throughput look responsive enough for interactive loops.

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

  • 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.

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Context
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Input Price
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Context
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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
39.4
MMLU Pro
87.5%
GPQA
83.0%
HLE
22.2%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
32.8
LiveCodeBench
0.810
SciCode
40.7%
Math

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

Math Index
82.7
AIME 2025
82.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
69.9%
TAU2
85.4%
TerminalBench Hard
28.8%
LCR
59.0%

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.