AI Tool Profile
MiniMax M3
MiniMax released M3 for coding, agent workflows, long-context reasoning, and native image and video understanding through MiniMax Code, Token Plans, and API access.
Verification & Sources
- Evidence state
- Source-verified
- Source links
- 6
- Freshness
- Checked August 4, 2026
- Last verified
- August 4, 2026
- Last updated
- July 9, 2026
What this evidence state means
- Definition
- The stated claim was checked against a named primary or authoritative source. This does not mean Kingy tested the product.
- Required provenance
- At least one public, named primary or authoritative source that directly supports the claim.
- Owner
- Kingy editorial reviewer
- Freshness rule
- Recheck within 30 days and after a material product, price, access, or source change.
- Disputes and corrections
- Use “Suggest a correction” on the record. Kingy editorial reviews the cited evidence, records material corrections, and changes or removes the state when it is not supported.
Key source checks
Suggest a correction
MiniMax M3 is an open-weight, natively multimodal model from MiniMax for coding, agent workflows, long-context reasoning, and image or video understanding. MiniMax officially released M3 on June 1, 2026.
What MiniMax M3 does
MiniMax says M3 uses its MiniMax Sparse Attention architecture and supports context windows of up to one million tokens. The model is designed for software development, tool use, multi-step agent work, and multimodal inputs rather than text-only chat.
The official release describes two operating modes: a thinking mode for more complex reasoning and agent tasks, and a faster non-thinking mode for latency-sensitive work such as conversation or code completion. Those capabilities make M3 relevant to teams evaluating coding assistants, research agents, long-document workflows, and applications that need to combine text with images or video.
Access and pricing
MiniMax provides M3 through MiniMax Code, its Token Plans, and the MiniMax API. API pricing varies with input length and service tier, while Token Plans package model usage into monthly subscriptions. Buyers should confirm the current rates and limits on MiniMax’s official pages before estimating production cost.
What to verify
Performance figures in MiniMax’s launch material are vendor-reported. Kingy.ai has not independently reproduced those benchmarks. Teams should test task reliability, latency, tool-call behavior, data handling, and total cost on their own workloads before moving M3 into production.
Official sources
The Kingy Brief
Follow The Kingy Brief.
One consequential launch, one pricing, limit, or shutdown change, one hands-on test, one exact prompt or Test Pack, and one try / watch / skip verdict.
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Main competitors
Frontier coding and agentic models: Claude Opus 5 and Claude Sonnet 5 (Anthropic), the GPT-5.6 family (OpenAI) and Gemini 3.1 Pro (Google). Among open-weight releases the closest structural comparisons are DeepSeek V4 and Alibaba's Qwen3 family, which compete on the same open-weight, long-context, coding-first positioning.
Tool Links
Related Kingy Links
Launch History
MiniMax M3
MiniMax released M3 for coding, agent workflows, long-context reasoning, and native image and video understanding through MiniMax Code, Token Plans, and API access.
- Launch readiness
- 7.7 / 10
- Demo evidence
- Not scored yet
- Creator-story fit
- Not scored yet
Score definitions and rubric
These are launch-record readiness heuristics, not product ratings.
Launch readiness
How complete and reviewable the launch record is, not the quality of the product.
Inputs and weights: Launch date 15%; qualifying source 10%; what launched 10%; demo 15%; category 10%; audience 10%; editorial assessment 10%; traction evidence 10%; creator or audience fit 10%.
Evidence inputs: Reviewed launch metadata, public source links, demo links, taxonomy, audience, editorial notes, and recorded traction signals.
Demo evidence
Whether the record contains useful, reviewable demonstration evidence; it is not a rating of product output quality.
Inputs and weights: Working demo URL 45%; video walkthrough 25%; clear description of what launched 10%; audience 10%; editorial assessment 10%.
Evidence inputs: Demo and video URLs plus the reviewed launch description, audience, and editorial notes.
Creator-story fit
Whether a launch has enough demonstrable evidence and audience relevance for a useful creator story; it does not predict views or guarantee coverage.
Inputs and weights: Demo evidence 25%; visual creator category 15%; audience 15%; editorial assessment 15%; traction evidence 10%; pricing clarity 10%; API or open-weight evidence 10%.
Evidence inputs: Reviewed demo, category, audience, editorial, traction, pricing, API, and open-weight fields.
- Scale
- 0.0–10.0. A present qualifying input receives its published weight; a missing input receives zero. Scores are rounded to one decimal.
- Assigned by
- Suggested by the deterministic field-completeness helper and assigned or approved by a Kingy editorial reviewer.
- Rubric and check date
- Rubric version P0-2026-08-10. The record’s “Last verified” date is the score check date. Checked: 2026-07-09.
- Confidence and missing data
- Confidence depends on source completeness. “Not scored yet” means no reviewed value; “Needs review” means the value or score set failed validation.
- Freshness
- Recalculate after a material launch, source, demo, pricing, audience, or traction change and during the record freshness review.
- Disputes
- Use “Suggest a correction” on the record and cite the relevant evidence. Commercial relationships cannot buy or alter a score.
MiniMax released M3, an open-weight model for coding, agent workflows, long-context reasoning, and native image and video understanding, available through MiniMax Code, Token Plans,…