MiniMax M3.1 Flash Review: 1M Context, Video Input, $0.15 per Million
Pick MiniMax M3.1 Flash for cheap long-context agent loops with screenshots and clips; so far we have run only launch checks, no scored tests.
Freedom Score
Not yet tested
- Price
- $0.263 per 1M mixed tokens (3 input : 1 output)
- Output speed
- 125 tok/s
- Success rate
- Collecting data
MiniMax M3.1 Flash is MiniMax's Flash-tier multimodal model, released on 19 October 2026. MiniMax pitches it as a long-context, multimodal model with tunable thinking depth, aimed at coding agents and computer workflows. On our API it is one OpenAI-compatible chat endpoint that reads text, images and video, holds 1,000,000 tokens of input and allows replies up to 524,288 tokens, with thinking counted inside that number.
- SpicyAPI Engineering
- Tested Oct 28, 2026
- Methodology v3.3 →
- We sell these models; scores ignore price →
The verdict
Two behaviours shape how you use it. Reasoning is always on: reasoningeffort accepts low, medium, high, xhigh or max (default max) and there is no none. And sampling is fixed by MiniMax, so temperature and topp are not accepted. A default request therefore reasons at full depth, and a small max_tokens can be spent on thinking before any visible text appears.
This is a launch-day review. On 28 October 2026 our platform ran a real request through the endpoint as part of launch: one neutral message, answered without streaming at low reasoning effort. It returned a complete answer. We have not run a quality blind test or a content-level test, and this page does not score the model.
Key facts
- Tested: one real launch request on 28 Oct 2026 (neutral prompt, low reasoning effort, no streaming), plus an integration check of media input. No scored runs yet.
- Price: $0.15 per million input tokens, $0.015 cached and $0.60 output. The live price is on the model page.
- Limits: 1,000,000-token context, replies up to 524,288 tokens (default 4,096, thinking included), text, image and video input, 256 messages and 128 tool definitions per request.
- Reasoning: reasoning_effort takes low, medium, high, xhigh or max (default max); reasoning cannot be disabled.
- Sampling: fixed by the model; temperature and top_p are not accepted.
- Sources: scoring rules are on our review methodology page, full parameters on the MiniMax M3.1 Flash model page.
What we liked and what held it back
What we liked
- A low per-token price for a 1M window. At $0.15 input and $0.60 output per million tokens, a long agent trace or a stack of documents is affordable to send in full.
- Five effort levels. Quick tool-loop steps and hard planning use the same model and the same code, with one field changed.
- Video and images in the prompt. A screenshot of an error, a product photo or a short screen recording goes straight into the message.
- A large reply ceiling. Up to 524,288 tokens in one reply, so long outputs do not need chunking.
- Tool calling is on the endpoint. Up to 128 function definitions with toolchoice and paralleltool_calls; we have not graded tool-use quality yet.
What held it back
- Reasoning cannot be disabled. There is no none, so even a simple rewrite pays for a reasoning pass; low is the lightest setting.
- max is the default effort. Every request reasons at full depth unless you set a lower level.
- No temperature or top_p. Use the prompt, reasoning_effort and stop sequences to shape output.
- Reasoning is paid for out of max_tokens. The default cap is 4,096; a small cap at max effort can run out before the answer starts.
- Mixed early reports. Some developers questioned how it performs on real agent work (see the community section), and we have no scored runs of our own yet.
What we tested
On 28 October 2026 our platform sent MiniMax M3.1 Flash one real request as part of launch: a single neutral user message, answered without streaming at low reasoning effort with a 1,024-token cap. It returned a complete answer, and the request was priced at the published rate. The endpoint also passed our pre-launch checks of its price table and request limits. During integration a media request was accepted and answered.
That is a working-endpoint check, not a benchmark. We have not compared its answers with another model, tested video on real clips, measured speed, or tried a prompt near the 1,000,000-token limit.
Prompts to start from
These starting prompts come from the model page's published examples. We have not graded the outputs, so they are a place to begin rather than prompts proven on this model.
1. Plan a reproduction. Hand it the issue report and a standard for evidence.
Inspect an application issue report and propose the next tool calls needed to reproduce it. Work from the material I provide, cite the relevant evidence, distinguish facts from assumptions, and identify missing information before proposing changes.Run this prompt on MiniMax M3.1 Flash →
2. A tool step. Confirms your function-calling loop end to end with a trivial function.
Call report_status with status ready.Settings that matter: use low or medium for routine agent steps and keep xhigh or max for planning that justifies the tokens; leave several thousand tokens of max_tokens headroom; send video inline and keep it under 14 MB combined.
How far it writes
We have not run our content-level test on MiniMax M3.1 Flash, so we have no result to report here. The model page states the position plainly: the platform adds no content filter of its own, content decisions come from the model, and behaviour can change between versions.
Not tested yet: the content-level ladder, multi-turn behaviour over a long session, languages other than English, video understanding on real clips, and tool-use quality over many steps.
Every character in a request should be an adult, keep real, identifiable people out of it, and you are responsible for the law where you publish.
What the community says
The launch thread was mostly about price and doubt. One reader wrote "Another day where the Pareto frontier is moved up and to the left!" while asking for wall-clock times that releases rarely show. Another questioned the benchmarks against what testers saw before the release: "The deepswe and terminalbench4.0 scores don't seem to jive with what many opencode users' experience has been of this model while it was being tested as "space bunny"."
Alternatives
- A Xiaomi option with a none setting? MiMo V2.6 Flash accepts reasoning_effort none and also takes audio input.
- A cheap model with a content-level review? DeepSeek V4.1 Flash reads 1,048,576 tokens; see the DeepSeek V4.1 Flash review.
- Another low-cost option? GLM 5.3 Flash is on the same API; compare it on your own prompts.
How MiniMax M3.1 Flash earns its Spicy Index
Each dimension below links to its full workings: the table row every point came from and why points were lost.
- Public leaderboards 40% of quality
- Not yet tested
- Role-play 15% of quality
- Not yet tested
- Creative writing 15% of quality
- 59.9
- Instruction following 15% of quality
- 95.0
- Long-context memory 15% of quality
- Not yet tested
- Capability 10.5% weight
- 52.0
- Freedom Score 30% weight
- Not yet tested
No exact match on a tracked public leaderboard yet. How it works
Our role-play suite hasn’t run on this model yet. How it works
Our long-context suite hasn’t run on this model yet. How it works
Freedom evidence is untested or insufficient. The Index retains the original quality and capability score, is marked preliminary and still receives a rank. Missing evidence is not zero. How it works
Freedom evidence is untested or insufficient. The Index retains the original quality and capability score, is marked preliminary and still receives a rank. Missing evidence is not zero.
Open the full test report →Capability, item by item
Points from the public scoring table. Upscaled tiers and limits we set ourselves don’t count.
- Context window (creator-published)Not declared
- 0.0 / 30
- Input modalities on SpicyAPIText, plus images
- 12.0 / 25
- Tool useTools and tool_choice
- 15.0 / 15
- Structured outputAccepted, schema adherence verified
- 15.0 / 15
- Reasoning control5 levels: low · medium · high · xhigh · max
- 10.0 / 15
What MiniMax M3.1 Flash can do on SpicyAPI
Straight from the live model specification on SpicyAPI. Some creators offer more in their own apps; this is what you can call through our API today.
| Feature | Chat |
|---|---|
| Context window | 1,000,000 tokens |
| Max output per request | 524,288 tokens |
| Reasoning levels | low · medium · high · xhigh · max |
| Tool calling | Yes |
| Structured output | Yes |
How long it really takes
Measured on real jobs over the last 30 days, from request to finished result. Times include our own test runs.
- Output speed
- 125 tok/sIncludes time to first token
- Success rate
- Collecting data
A time appears once at least 10 completed jobs are in. Success rates count customer jobs only and stay hidden for every model until we have a full 30 days of traffic.
What people say about MiniMax M3.1 Flash
0 positive · 1 mixed · 1 negative
Another day where the Pareto frontier is moved up and to the left!
hacker-news· Oct 19, 2026· MixedSource
The deepswe and terminalbench4.0 scores don't seem to jive with what many opencode users' experience has been of this model while it was being tested as "space bunny".
hacker-news· Oct 19, 2026· NegativeSource
Quotes are verbatim from public posts, linked to the original. They are not ratings and don’t feed any score.
Models to consider instead of MiniMax M3.1 Flash
Xiaomi
MiMo V2.6 Flash
A Xiaomi option with a none setting?
- Spicy Index
- 76.2+2.6
- Freedom Score
- —
- Price
- $0.175−33%/ 1M tokens · 3:1 input/output
DeepSeek
DeepSeek V4.1 Flash
A cheap model with a content-level review?
- Spicy Index
- 81.9+8.3
- Freedom Score
- 90.4
- Price
- $0.525+100%/ 1M tokens · 3:1 input/output
Z.ai
GLM 5.3 Flash
Another low-cost option?
- Spicy Index
- 72.9−0.7
- Freedom Score
- 64.4
- Price
- $0.202−23%/ 1M tokens · 3:1 input/output
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MiniMax M3.1 Flash FAQ
Try MiniMax M3.1 Flash yourself
Set every parameter in the Playground and see the exact price before you run it.
$0.263 per 1M mixed tokens (3 input : 1 output)
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