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
| Provider | Quantization | Input / 1M | Output / 1M | Cache read / 1M | Context | p50 latency | p50 throughput | Uptime (1d) |
|---|---|---|---|---|---|---|---|---|
| CoreWeave | fp4 | $0.23 / 1M | $0.96 / 1M | $0.05 / 1M | 262,144 tokens | 0.93 s | 71 tok/s | 100.00% |
| GMICloud | fp8 | $0.24 / 1M | $0.96 / 1M | $0.048 / 1M | 1,048,576 tokens | 2.46 s | 66 tok/s | 100.00% |
| DeepInfra | fp8 | $0.28 / 1M | $1.1 / 1M | $0.056 / 1M | 524,288 tokens | 1.44 s | 13 tok/s | 100.00% |
| StreamLake | fp8 | $0.3 / 1M | $1.2 / 1M | $0.06 / 1M | 1,000,000 tokens | 1.44 s | 82 tok/s | 99.49% |
| Venice | fp8 | $0.3 / 1M | $1.2 / 1M | $0.06 / 1M | 524,288 tokens | 8.10 s | 35 tok/s | 90.87% |
| Together | unknown | $0.3 / 1M | $1.2 / 1M | $0.06 / 1M | 524,288 tokens | 0.57 s | 61 tok/s | 100.00% |
| Parasail | fp8 | $0.3 / 1M | $1.2 / 1M | $0.06 / 1M | 1,048,576 tokens | 1.42 s | 61.5 tok/s | 99.61% |
| AtlasCloud | fp8 | $0.3 / 1M | $1.2 / 1M | $0.06 / 1M | 524,300 tokens | 1.11 s | 76 tok/s | 98.95% |
| NovitaAI | fp8 | $0.3 / 1M | $1.2 / 1M | $0.06 / 1M | 1,000,000 tokens | 1.54 s | 68 tok/s | 100.00% |
| MiniMax | fp8 | $0.3 / 1M | $1.2 / 1M | $0.06 / 1M | 524,288 tokens | 1.10 s | 88 tok/s | 100.00% |
| MARA | unknown | $0.6 / 1M | $2.4 / 1M | No verified rate | 1,048,576 tokens | 1.51 s | 115 tok/s | 100.00% |
| SambaNova | unknown | $0.6 / 1M | $2.4 / 1M | $0.06 / 1M | 1,048,576 tokens | 2.01 s | 67 tok/s | 100.00% |
| ModelRun [by Modular] | fp4 | $0.75 / 1M | $3 / 1M | $0.15 / 1M | 1,048,576 tokens | 2.07 s | 92 tok/s | 100.00% |
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 category | Elo | Win rate | Rank |
|---|---|---|---|
| agenticgamedev | 1158 | 44.6% | #19 |
| androidnative | 1171 | 44.3% | #23 |
| fullstack | 1196 | 47.3% | #19 |
| htmlslides | 1180 | 46.6% | #12 |
| mobileapps | 1167 | 46% | #23 |
| python-pptxslides | 1204 | 46.8% | #14 |
| webapps | 1192 | 47.1% | #21 |
| 3d | 1210 | 50.2% | #44 |
| asciiart | 1163 | 46.8% | #35 |
| codecategories | 1252 | 50.9% | #38 |
| dataviz | 1237 | 51.3% | #41 |
| gamedev | 1218 | 46% | #44 |
| svg | 1162 | 45.2% | #39 |
| uicomponent | 1250 | 50.9% | #39 |
| website | 1260 | 51.9% | #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.
| Provider | GPQA Diamond | TAU-Bench Airline | Runs |
|---|---|---|---|
| AtlasCloud | 92.3% | 77.4% | 4 |
| auto-routing | 90.78% | 75.58% | 5 |
| CoreWeave | 90.95% | 73.99% | 6 |
| DeepInfra | 92.24% | 76.53% | 2 |
| GMICloud | 92.39% | 76.19% | 5 |
| MARA | 82.68% | 74.54% | 5 |
| MiniMax | 91.84% | 74.88% | 5 |
| ModelRun [by Modular] | 90.67% | 74.97% | 8 |
| NovitaAI | 91.38% | 76.1% | 5 |
| Parasail | 89.78% | 75.68% | 5 |
| SambaNova | 82.24% | 73.38% | 3 |
| StreamLake | 91.84% | — | 6 |
| Together | 91.46% | 77.71% | 5 |
| Venice | 91.29% | 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
- MiniMax M3 third-party catalog record (catalog, checked 2026-10-13)
- Third-party model catalog methodology (methodology, checked 2026-10-13)
- MiniMax official model documentation (primary, checked 2026-10-13)
- Artificial Analysis capability indices methodology (benchmark, checked 2026-10-13)
- Design Arena leaderboard and methodology (benchmark, checked 2026-10-13)
- MiniMax M3 third-party catalog record (benchmark, checked 2026-10-13)
- MiniMax M3 provider or route reference (primary, checked 2026-10-13)