Model profile
MiniMax
New in 2026

MiniMax: MiniMax M3

MiniMax: MiniMax M3 is a budget-priced multimodal generalist from MiniMax with fast runtime profile, large context posture, and the clearest fit around long-context research / agent workflows.

Best for: Long-context research / Agent workflowsFast latencyLarge contextBudget pricing
Intelligence
45.4

Benchmark blend

Coding
58.6

Dev workflow signal

Context
1049K Tokens

Large

Input Price
$0.30

Budget tier

Decision snapshot
68

MiniMax: MiniMax M3 currently reads as a budget multimodal option with large context and a fast runtime profile.

Overall profile
Strong all-rounder
Best for
Long-context research / Agent workflows
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial AnalysisVision signal

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
45.4
45

General reasoning and benchmark headroom.

Situational
Speed
104 tok/s
76

TTFT 1.17s

Competitive
Context
1049K Tokens
100

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 M3 in one narrative

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

Strong all-rounderCoding score 59Math score N/AVision enabled

MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding,...

Identity

MiniMax multimodal profile

Positioning

Long-context research / Agent workflows with large 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.

  • Vision-capable routing opens up multimodal review and extraction workflows.

  • Latency and throughput look responsive enough for interactive loops.

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

Best fit
  • Image-grounded review, multimodal extraction, and UI audit workflows.

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

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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
45.4
GPQA
92.9%
HLE
39.0%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
58.6
SciCode
45.4%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
82.9%
TAU2
88.9%
TerminalBench Hard
42.4%
LCR
80.3%

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
1049K Tokens
Vision
Enabled
Modalities
image, text, video
Tokenizer
Other
Max Completion
512000
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoninglogit_biaslogprobsmax_tokensmin_ppresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p
Input Modalities
imagetextvideo
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.