MiniMax: MiniMax M3
minimax/minimax-m3
Model weightsMiniMax-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.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick: Balanced (price + speed), Nitro (fastest), or Floor (cheapest).
| $0.230 | $0.96 | $0.0500 | 0.49s | 115 tok/s | ||
| $0.300 | $1.20 | $0.0600 | 0.80s | 52 tok/s | ||
20% off | $0.30$0.240 | $1.20$0.96 | $0.06$0.0480 | 2.87s | 54 tok/s | |
| $0.300 | $1.20 | $0.0600 | 1.71s | 40 tok/s | ||
| $0.300 | $1.20 | $0.0600 | 0.74s | 99 tok/s | ||
| $0.280 | $1.10 | $0.0560 | 1.06s | 42 tok/s | ||
Fast | $0.450 | $1.80 | $0.0900 | 0.75s | 105 tok/s | |
Not used in Standard routing:Why these endpoints are not used | ||||||
| $0.224 | $0.88 | $0.0448 | 1.80s | 29 tok/s | ||
- Input $0.23 / Output $0.96115 tok/s100.00% uptime
- Input $0.30 / Output $1.2052 tok/s99.09% uptime
- Input $0.24 / Output $0.9620% off54 tok/s99.92% uptime
- Input $0.30 / Output $1.2040 tok/s100.00% uptime
- Input $0.30 / Output $1.2099 tok/s99.39% uptime
Input $0.28 / Output $1.1042 tok/s99.14% uptime- Input $0.45 / Output $1.80105 tok/s99.75% uptimeFast
- Not used in Standard routing:Why these endpoints are not used
Input $0.224 / Output $0.8829 tok/s98.43% uptimeFlex
Pricing
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Weighted average
MiniMax M3 — Price history
| $0.0671 | $0.959 | 90.1% | 8.5% | ||
| $0.0686 | $1.200 | 96.4% | 79.3% | ||
| $0.0903 | $0.959 | 77.6% | 6.8% | ||
| $0.1028 | $1.099 | 79.1% | 0.8% | ||
| $0.1252 | $1.199 | 72.8% | 0.7% | ||
| $0.1803 | $1.200 | 49.9% | 1.0% | ||
Fast | $0.1934 | $1.800 | 71.3% | 2.9% | |
Not used in Standard routing:Why these endpoints are not used | |||||
| $0.1110 | $0.879 | 62.9% | 0.1% | ||
- Effective in $0.0671 / Effective out $0.959Token share8.5%
- Effective in $0.0686 / Effective out $1.200Token share79.3%
- Effective in $0.0903 / Effective out $0.959Token share6.8%
- Effective in $0.1028 / Effective out $1.099Token share0.8%
- Effective in $0.1252 / Effective out $1.199Token share0.7%
- Effective in $0.1803 / Effective out $1.200Token share1.0%
- Effective in $0.1934 / Effective out $1.800FastToken share2.9%
- Not used in Standard routing:Why these endpoints are not used
- Effective in $0.1110 / Effective out $0.879FlexToken share0.1%
Performance
Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).
P50, best across providers
P50, best provider
Uptime
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Availability over the last 3 days
Last 72 hoursAvailability over the last 24 hours
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.
Benchmarks
Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.
| Source | Benchmark | Score |
|---|---|---|
| Artificial Analysis | MiniMax-M3 Intelligence Index | 18.9 |
| Artificial Analysis | MiniMax-M3 Coding Index | 58.6 |
| Artificial Analysis | MiniMax-M3 Agentic Index | 29.5 |
| Artificial Analysis | MiniMax-M3 GPQA Diamond | 92.9% |
| Artificial Analysis | MiniMax-M3 HLE | 39.0% |
| Artificial Analysis | MiniMax-M3 IFBench | 82.9% |
| Artificial Analysis | MiniMax-M3 τ²-Bench Telecom | 88.9% |
| Artificial Analysis | MiniMax-M3 AA-LCR | 83.0% |
| Artificial Analysis | MiniMax-M3 τ-Bench Banking | 15.3% |
| Artificial Analysis | MiniMax-M3 GDPval-AA | 37.5% |
| Artificial Analysis | MiniMax-M3 CritPt | 3.7% |
| Artificial Analysis | MiniMax-M3 SciCode | 47.1% |
| Artificial Analysis | MiniMax-M3 Terminal-Bench Hard | 42.4% |
| Artificial Analysis | MiniMax-M3 Terminal-Bench 4.0 | 2.0% |
| Artificial Analysis | MiniMax-M3 Terminal-Bench 2.1 | 65.2% |
| Artificial Analysis | MiniMax-M3 AA-Omniscience Accuracy | 44.9% |
| Artificial Analysis | MiniMax-M3 AA-Omniscience Non-Hallucination Rate | 8.6% |
| Design Arena | MiniMax M3 Agents Arena Agenticgamedev Elo | 1110 |
| Design Arena | MiniMax M3 Agents Arena Androidnative Elo | 1156 |
| Design Arena | MiniMax M3 Agents Arena Blender3d Elo | 1089 |
| Design Arena | MiniMax M3 Agents Arena Codetovideo Elo | 1202 |
| Design Arena | MiniMax M3 Agents Arena Full Stack Elo | 1173 |
| Design Arena | MiniMax M3 Agents Arena Htmlslides Elo | 1076 |
| Design Arena | MiniMax M3 Agents Arena Mobile Apps Elo | 1169 |
| Design Arena | MiniMax M3 Agents Arena Pdf Elo | 1262 |
| Design Arena | MiniMax M3 Agents Arena Python-Pptxslides Elo | 1171 |
| Design Arena | MiniMax M3 Agents Arena Spreadsheet Elo | 1161 |
| Design Arena | MiniMax M3 Agents Arena Webapps Elo | 1187 |
| Design Arena | MiniMax M3 Models Arena 3D Elo | 1190 |
| Design Arena | MiniMax M3 Models Arena Asciiart Elo | 1149 |
| Design Arena | MiniMax M3 Models Arena Code Categories Elo | 1236 |
| Design Arena | MiniMax M3 Models Arena Data Visualization Elo | 1229 |
| Design Arena | MiniMax M3 Models Arena Game Development Elo | 1200 |
| Design Arena | MiniMax M3 Models Arena SVG Elo | 1146 |
| Design Arena | MiniMax M3 Models Arena UI Component Elo | 1235 |
| Design Arena | MiniMax M3 Models Arena Website Elo | 1249 |
| Vals AI | MiniMax-M3 Access to Justice Benchmark | 69.8% |
| Vals AI | MiniMax-M3 Code Migration v1.1 | 20.9% |
| Vals AI | MiniMax-M3 CorpFin v2 | 68.1% |
| Vals AI | MiniMax-M3 CyberBench v1.1 | 65.0% |
| Vals AI | MiniMax-M3 EMB v1.1 | 59.8% |
| Vals AI | MiniMax-M3 Finance Agent v2.1 | 54.3% |
| Vals AI | MiniMax-M3 Vals Finance Index | 48.4% |
| Vals AI | MiniMax-M3 Financial Coaching Bench | 69.0% |
| Vals AI | MiniMax-M3 GPQA Diamond | 92.7% |
| Vals AI | MiniMax-M3 Harvey's Legal Agent Benchmark v1 | 4.2% |
| Vals AI | MiniMax-M3 Immigration | 57.5% |
| Vals AI | MiniMax-M3 Vals Inference Provider Index | 53.5% |
| Vals AI | MiniMax-M3 LiveCodeBench | 82.2% |
| Vals AI | MiniMax-M3 LegalBench | 85.4% |
| Vals AI | MiniMax-M3 Legal Research Bench | 29.8% |
| Vals AI | MiniMax-M3 MedCode | 46.3% |
| Vals AI | MiniMax-M3 MedScribe | 87.3% |
| Vals AI | MiniMax-M3 MMLU Pro | 84.2% |
| Vals AI | MiniMax-M3 MMMU Pro | 81.2% |
| Vals AI | MiniMax-M3 MortgageTax | 68.4% |
| Vals AI | MiniMax-M3 ProofBench v1.1 | 18.0% |
| Vals AI | MiniMax-M3 Public Benefits Bench v1.1 | 64.1% |
| Vals AI | MiniMax-M3 SAGE | 85.5% |
| Vals AI | MiniMax-M3 SkillsBench | 51.5% |
| Vals AI | MiniMax-M3 SWE-bench | 75.0% |
| Vals AI | MiniMax-M3 SWE-bench Multimodal | 33.3% |
| Vals AI | MiniMax-M3 Tax Agent Bench | 20.6% |
| Vals AI | MiniMax-M3 TaxEval v2 | 72.7% |
| Vals AI | MiniMax-M3 Terminal-Bench 4.0 | 1.0% |
| Vals AI | MiniMax-M3 Vals Index | 36.5% |
| Vals AI | MiniMax-M3 Vals Multimodal Index | 60.0% |
| Vals AI | MiniMax-M3 Vibe Code Bench 1-100 | 9.2% |
| Vals AI | MiniMax-M3 Vibe Code Bench v1.1 | 47.6% |
Apps
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.
Activity
Token volume and request traffic to this model over time.
API
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
Not currently available through EU in-region routing. In-region routing docs
Frequently asked questions
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.
MiniMax M3 costs $0.23/M input tokens and $0.96/M output tokens, with separate rates for Cache Read at $0.05/M tokens.
MiniMax M3 has a 1,048,576 token context window. It supports up to 262,144 completion tokens.
Yes. MiniMax M3 accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.
MiniMax M3 accepts text, images, and video as input and returns text.
MiniMax M3 is served by 6 providers on OpenRouter: DeepInfra, CoreWeave, GMICloud, Venice, Parasail, and MiniMax. Requests are routed to the best available provider, with automatic failover to the others, and you can pin or exclude providers with provider routing.
MiniMax M3 was released on May 31, 2026.