MiniMax M3 API model profile

Evidence snapshot:

MiniMax M3 is a MiniMax model profile with a 1,048,576-token context window. The dated catalog snapshot records text, image, video modalities and interfaces for prompt caching, reasoning controls, structured output, text, text generation.

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, and tool use. It is built on MiniMax Sparse Attention (MSA), which replaces full attention with KV-block selection to cut per-token compute at long context — roughly 1/20 the cost of the previous generation at 1M tokens, with substantially faster prefill and decode while retaining quality across most tasks. Trained as a native multimodal model on interleaved data and tuned for multi-turn, production-like collaboration via an interactive user-simulator framework, the model is oriented toward sustained, multi-step tasks rather than single-turn execution.

MiniMax M3 is worth reaching for when you need a route carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support.

MiniMax M3 is available under the Kendr alias kc-minimax-m3. Current account availability and customer credit quotes come from Kendr's live public model API and applicable account policy.

MiniMax M3 accepts text, image, and video and returns text. The snapshot records prompt caching, reasoning controls, structured output, text, text generation, tool calling, video input, vision, and web search as capabilities or interfaces.

The dated reference snapshot lists $0.23 input and $0.96 output per million tokens. Cached input is $0.048 per million tokens. The separately published provider-route reference is $0.49 / $0.95.

The 2026-10-13 snapshot includes 3 Artificial Analysis indexes, 15 Design Arena categories, and AutoExacto results for 14 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.

MiniMax M3 ranked #23 in the cited trailing-7-day third-party catalog usage dataset as of 2026-10-13. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

Profile data was reviewed for the 2026-10-13 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.

Profile facts

Profile type
Kendr API model
Kendr API alias
kc-minimax-m3
Model developer
MiniMax
Kendr route provider
MiniMax
Context window
1,048,576 tokens
Snapshot date
2026-10-13
Knowledge cutoff
Not disclosed
Reference catalog ID
minimax/minimax-m3

When to pick MiniMax M3

MiniMax M3 is worth reaching for when you need a route carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support.

Each condition below is derived from this model's own values in the 2026-10-13 snapshot, compared only against models publishing the same field.

  • Reach for it when a whole corpus has to fit in one prompt. The 1.05M-token context window is in the top quarter of the 167 models publishing a limit here, so long documents can go in whole rather than chunked.
  • Reach for it when the input includes images. The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described.
  • Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.

Context, modalities, and identity

MiniMax M3 accepts text, image, and video and returns text. The snapshot records prompt caching, reasoning controls, structured output, text, text generation, tool calling, video input, vision, and web search as capabilities or interfaces.

Model developer
MiniMax
Kendr route provider
MiniMax
Context window
1,048,576 tokens
Maximum output
512,000 tokens
Input modalities
text, image, and video
Output modalities
text
Knowledge cutoff
Not disclosed

Model overview

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, and tool use. It is built on MiniMax Sparse Attention (MSA), which replaces full attention with KV-block selection to cut per-token compute at long context — roughly 1/20 the cost of the previous generation at 1M tokens, with substantially faster prefill and decode while retaining quality across most tasks. Trained as a native multimodal model on interleaved data and tuned for multi-turn, production-like collaboration via an interactive user-simulator framework, the model is oriented toward sustained, multi-step tasks rather than single-turn execution.

Reference model ID
minimax/minimax-m3
Canonical version
minimax/minimax-m3-20260531
Hugging Face ID
MiniMaxAI/Minimax-M3

Capabilities and supported controls

Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.

Supported parameters
frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p
  • prompt caching
  • reasoning controls
  • structured output
  • text
  • text generation
  • tool calling
  • video input
  • vision
  • web search

Dated pricing snapshot

The dated reference snapshot lists $0.23 input and $0.96 output per million tokens. Cached input is $0.048 per million tokens. The separately published provider-route reference is $0.49 / $0.95.

Reference figures are dated 2026-10-13; the live Kendr quote can differ by selected provider route, context tier, caching, tools, region, and current rate card.

Price date
2026-10-13
Input
$0.23 per 1M tokens
Cached input
$0.048 per 1M tokens
Output
$0.96 per 1M tokens

Provider routes, performance, and uptime

The snapshot retains 13 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.

Active provider endpoints
13
Best p50 latency
0.57 s
Best p50 throughput
115 tok/s
Availability with routing
99.43% over the sampled window
Availability without routing
96.51% over the sampled window
Performance date
2026-10-13
ProviderQuantizationInput / 1MOutput / 1MCache read / 1MContextp50 latencyp50 throughputUptime (1d)
CoreWeavefp4$0.23 / 1M$0.96 / 1M$0.05 / 1M262,144 tokens0.93 s71 tok/s100.00%
GMICloudfp8$0.24 / 1M$0.96 / 1M$0.048 / 1M1,048,576 tokens2.46 s66 tok/s100.00%
DeepInfrafp8$0.28 / 1M$1.1 / 1M$0.056 / 1M524,288 tokens1.44 s13 tok/s100.00%
StreamLakefp8$0.3 / 1M$1.2 / 1M$0.06 / 1M1,000,000 tokens1.44 s82 tok/s99.49%
Venicefp8$0.3 / 1M$1.2 / 1M$0.06 / 1M524,288 tokens8.10 s35 tok/s90.87%
Togetherunknown$0.3 / 1M$1.2 / 1M$0.06 / 1M524,288 tokens0.57 s61 tok/s100.00%
Parasailfp8$0.3 / 1M$1.2 / 1M$0.06 / 1M1,048,576 tokens1.42 s61.5 tok/s99.61%
AtlasCloudfp8$0.3 / 1M$1.2 / 1M$0.06 / 1M524,300 tokens1.11 s76 tok/s98.95%
NovitaAIfp8$0.3 / 1M$1.2 / 1M$0.06 / 1M1,000,000 tokens1.54 s68 tok/s100.00%
MiniMaxfp8$0.3 / 1M$1.2 / 1M$0.06 / 1M524,288 tokens1.10 s88 tok/s100.00%
MARAunknown$0.6 / 1M$2.4 / 1MNo verified rate1,048,576 tokens1.51 s115 tok/s100.00%
SambaNovaunknown$0.6 / 1M$2.4 / 1M$0.06 / 1M1,048,576 tokens2.01 s67 tok/s100.00%
ModelRun [by Modular]fp4$0.75 / 1M$3 / 1M$0.15 / 1M1,048,576 tokens2.07 s92 tok/s100.00%

Benchmark evidence and limitations

The 2026-10-13 snapshot includes 3 Artificial Analysis indexes, 15 Design Arena categories, and AutoExacto results for 14 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.

Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

Intelligence Index
29.2
Coding Index
58.6
Agentic Index
29.5
AutoExacto coverage
14 provider observations over 32 days
Design Arena categoryEloWin rateRank
agenticgamedev115844.6%#19
androidnative117144.3%#23
fullstack119647.3%#19
htmlslides118046.6%#12
mobileapps116746%#23
python-pptxslides120446.8%#14
webapps119247.1%#21
3d121050.2%#44
asciiart116346.8%#35
codecategories125250.9%#38
dataviz123751.3%#41
gamedev121846%#44
svg116245.2%#39
uicomponent125050.9%#39
website126051.9%#37

AutoExacto provider benchmarks

Rolling provider observations from the cited third-party benchmark view. The lookback is 32 days; GPQA Diamond and TAU-Bench Airline measure different abilities and should not be blended into one score.

ProviderGPQA DiamondTAU-Bench AirlineRuns
AtlasCloud92.3%77.4%4
auto-routing90.78%75.58%5
CoreWeave90.95%73.99%6
DeepInfra92.24%76.53%2
GMICloud92.39%76.19%5
MARA82.68%74.54%5
MiniMax91.84%74.88%5
ModelRun [by Modular]90.67%74.97%8
NovitaAI91.38%76.1%5
Parasail89.78%75.68%5
SambaNova82.24%73.38%3
StreamLake91.84%—6
Together91.46%77.71%5
Venice91.29%74.64%5

Popularity and market context

MiniMax M3 ranked #23 in the cited trailing-7-day third-party catalog usage dataset as of 2026-10-13. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

Third-party catalog rank
#23
Global market share
Not inferred
Observation date
2026-10-13

OpenAI-compatible API example

This example applies to the published Kendr alias on this hosted profile. Check the live public catalog before use.

curl https://api.kendr.org/v1/chat/completions \
  -H "Authorization: Bearer $KENDR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"kc-minimax-m3","messages":[{"role":"user","content":"Hello"}]}'

Frequently asked questions

When should I use MiniMax M3?

MiniMax M3 is worth reaching for when you need a route carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support. Reach for it when a whole corpus has to fit in one prompt: The 1.05M-token context window is in the top quarter of the 167 models publishing a limit here, so long documents can go in whole rather than chunked. Reach for it when the input includes images: The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.

What is MiniMax M3?

MiniMax M3 is a MiniMax model profile with a 1,048,576-token context window. The dated catalog snapshot records text, image, video modalities and interfaces for prompt caching, reasoning controls, structured output, text, text generation.

What context window does MiniMax M3 have?

The dated profile lists 1,048,576 tokens of context and up to 512,000 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.

What benchmark evidence is available for MiniMax M3?

The 2026-10-13 snapshot includes 3 Artificial Analysis indexes, 15 Design Arena categories, and AutoExacto results for 14 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

What is the Kendr API alias for MiniMax M3?

Use kc-minimax-m3 as the model value. The live public model API and signed-in catalog remain authoritative for route availability and customer credit quotes.

How is MiniMax M3 priced on Kendr?

The dated reference snapshot lists $0.23 input and $0.96 output per million tokens. Cached input is $0.048 per million tokens. The separately published provider-route reference is $0.49 / $0.95. Kendr applies one 5% markup to configured provider model cost; the live quote and settled routing receipt are authoritative for a request.

Sources and evidence dates

  1. MiniMax M3 third-party catalog record (catalog, checked 2026-10-13)
  2. Third-party model catalog methodology (methodology, checked 2026-10-13)
  3. MiniMax official model documentation (primary, checked 2026-10-13)
  4. Artificial Analysis capability indices methodology (benchmark, checked 2026-10-13)
  5. Design Arena leaderboard and methodology (benchmark, checked 2026-10-13)
  6. MiniMax M3 third-party catalog record (benchmark, checked 2026-10-13)
  7. MiniMax M3 provider or route reference (primary, checked 2026-10-13)