MiMo-V2.6-Pro Guide: Performance, Pricing & Providers

API Review Summary (October 2026)

Attribute	Value
Developer	Xiaomi
Release Date	September 21, 2026
Model Type	Reasoning model (open weights)
Artificial Analysis Intelligence Index	30 — ranks #3 of 21 models in AA’s “Large” (>150B parameter) class, where the class median is 23
Speed	53.4 output tokens/sec (class median: 73.9 t/s)
Latency (TTFT)	3.16s (class median: 1.67s)
Context Window	1M tokens (~1,500 A4 pages)
Modalities	Text / image / speech / video input → text output
Model Size	1.02T total params, 42B active (MoE)
License	MIT (commercial use allowed)
Pricing	$0.435/1M input, $0.87/1M output (list); DeepInfra’s current discounted rate is $0.387/1M input, $0.783/1M output
Cache Discount	99%
Cost per Intelligence Index Task	$0.26
Providers	Listed across 27+ providers in the DeepInfra model catalog and aggregators such as models.dev; independently benchmarked by Artificial Analysis across 4 providers (Xiaomi, DeepInfra, PrimaLabs, GMI); routed through 5 providers on OpenRouter

MiMo-V2.6-Pro is Xiaomi’s flagship entry in the large language model market and one of the stronger open-weight models currently tracked by Artificial Analysis. Released in September 2026, this sparse Mixture-of-Experts (MoE) model packs 1.02 trillion total parameters while activating only 42 billion during inference — a design that balances raw capability with practical deployment cost.

On Artificial Analysis’s current Intelligence Index methodology (v5.0), MiMo-V2.6-Pro scores 30, placing it #3 of 21 models in the “Large” (over 150 billion parameter) size class, where the median score is 23 — solidly above the class median, though not the single highest-scoring model in that class. The model supports native multimodal input (text, image, video, and audio) with a 1-million-token context window, all under an MIT license that permits commercial use.

Note: Artificial Analysis periodically rescales the Intelligence Index as it adds or swaps evaluations (the current version, v5.0, draws on nine evaluations including AA-Briefcase v1.1, Terminal-Bench 4.0, and HLE-Diamond). Scores from different index versions are not directly comparable, so any cross-model comparison in this guide reflects the same, current methodology.

This guide evaluates MiMo-V2.6-Pro across the dimensions that matter for production deployment: intelligence benchmarks, pricing economics, speed and latency, provider options, and practical use cases.

Model Capabilities and Provider Comparisons

How MiMo-V2.6-Pro Compares to the Competition

MiMo-V2.6-Pro delivers strong agentic and tool-use performance at a fraction of proprietary model costs, which is what makes it attractive for high-volume agentic and coding workloads. The benchmark figures below are Xiaomi’s own published comparisons rather than independently reproduced Artificial Analysis results, and should be read as vendor-reported.

Benchmark	MiMo-V2.6-Pro	Claude Opus 5	GPT-5.6 Sol	GPT-6 Astra
DeepSWE v1.1	71.9	74.0	73.0	74.0
AutomationBench v1.0.6	53.1	50.3	45.8	52.0
Terminal Bench 2.1	89.9	89.1	88.8	89.9
Terminal Bench 4.0	34.9	49.0	39.9	59.6
Toolathlon-Verified	76.9	80.6	74.9	—
ExploitBench	47.9	70.0	78.5	—
CyberGym	94.0	—	—	—
JobBench	62.0	65.7	45.4	—

Key Takeaways

• Strengths: MiMo-V2.6-Pro performs well on general agentic tasks, tool use, and automation benchmarks — on several of these it matches or exceeds Claude Opus 5 and GPT-5.6 Sol, per Xiaomi’s figures.
• Weaknesses: It falls behind on the most demanding long-horizon coding benchmark (Terminal Bench 4.0) and on security exploitation (ExploitBench).
• Value proposition: It performs close to several frontier models at a meaningfully lower cost, and is one of the stronger-scoring open-weights options on Artificial Analysis’s current Intelligence Index, without being the single highest-ranked one.

The jump from V2.5 to V2.6 is substantial on Xiaomi’s own figures: DeepSWE v1.1 improved from 19.0 to 71.9, and Terminal Bench 4.0 went from 1.5 to 34.9.

Model Architecture, Weights, and Openness

Model Size: Total and Active Parameters

MiMo-V2.6-Pro uses a sparse Mixture-of-Experts architecture:

Specification	Value
Total Parameters	1.02 trillion
Active Parameters	42 billion (per forward pass)
Architecture	Sparse MoE transformer
Routed Experts	384 total, 8 activated per token
Layers	70 total (60 sliding-window attention + 10 global attention)
Hidden Size	6,144
Vision Encoder	681M parameters (MiMo ViT, 28 layers)
Audio Encoder	308M parameters

Open Weights vs. Proprietary

MiMo-V2.6-Pro is fully open weights, with the checkpoint available on Hugging Face as XiaomiMiMo/MiMo-V2.6-Pro-RL.

Model	Weights	License	Commercial Use
MiMo-V2.6-Pro	Open	MIT	Allowed
Claude Opus 5	Proprietary	Closed	Via API only
GPT-6 Astra	Proprietary	Closed	Via API only
Grok 4.7	Proprietary	Closed	Via API only

License and Model Weights

• License: MIT (fully permissive for commercial and research use)
• Weights location: Hugging Face (XiaomiMiMo/MiMo-V2.6-Pro-RL)
• Download size: ~573 GB (packed 8-bit container, with FP8/BF16 for select tensors)
• Self-hosting requirement: A Q4-class build would need roughly 612 GB of VRAM — beyond any consumer machine, and realistically a multi-GPU server or API access.

Openness Profile

• Weights are publicly available
• MIT license permits commercial use
• Model card and technical documentation are provided
• Available via multiple third-party API providers
• The exact quantization scheme used in the published checkpoint is not fully disclosed

Context Window and Token Use

Model	Context Window	Equivalent
MiMo-V2.6-Pro	1,048,576 tokens (~1M)	~1,500 A4 pages
MiMo-V2.6-Flash	1,048,576 tokens (~1M)	~1,500 A4 pages
Max Output	131,072 tokens (~128K)	—

The 1M-token context window enables full codebase analysis in a single call, long video transcripts and multi-turn agent traces, and extended document processing or RAG workflows.

MiMo-V2.6-Pro uses a custom tokenizer optimized for multilingual text (strong Chinese and English support), code across multiple programming languages, and multimodal content encoding. Artificial Analysis rates its output verbosity at 3 out of 4 — somewhat more verbose than peer models on the Intelligence Index benchmark suite, which is worth factoring into cost estimates for reasoning-heavy workloads.

Pricing and Cost Metrics

Providers and Pricing Overview

Provider	Input (per 1M)	Output (per 1M)	Context
Xiaomi API	$0.435	$0.87	1M
ClinePass	$0.43	$0.87	1M
LLM Gateway	$0.43	$0.87	1M
AIHubMix	$0.48	$0.96	1M
above.dev	$0.51	$1.02	1M
Neuralwatt	$0.87	$1.74	1M

Provider pricing changes frequently and the figures above weren’t independently re-verified for this guide — confirm current rates directly with each provider before relying on them.

Cost per Intelligence Index Task

Metric	MiMo-V2.6-Pro
Cost per Intelligence Index Task	$0.26
Blended price (7:2:1 cache:input:output ratio)	$0.18 per 1M tokens

MiMo-V2.6-Pro’s cost per Intelligence Index task is meaningfully lower than several proprietary frontier models, which Artificial Analysis generally places in the roughly $1–$6 per task range — though that comparison depends on which specific model and reasoning-effort setting you compare against. For a sense of how this stacks up against another recent large reasoning model, see our DeepSeek V4 Pro pricing and providers breakdown.

Pricing: Cache Hit Input and Output

Pricing Type	Rate
Standard Input	$0.435 / 1M tokens
Cached Input	$0.0036 / 1M tokens — see how prompt caching works
Cache Discount	99%
Output	$0.87 / 1M tokens

Speed, Latency, and Response Times

Output Speed

Metric	MiMo-V2.6-Pro	Class Median
Output Speed	53.4 tokens/sec	73.9 tokens/sec

Artificial Analysis rates MiMo-V2.6-Pro’s throughput below average for its size class.

Latency: Time to First Token (TTFT)

Metric	MiMo-V2.6-Pro	Class Median
TTFT	3.16 seconds	1.67 seconds
p95 TTFT (Xiaomi API, 7-day trailing)	5.31 seconds	—

Because of the model’s scale (1.02T parameters), end-to-end response times run longer than for smaller models. That latency trade-off is effectively the cost of accessing this level of agentic capability at open-weights pricing — it’s worth benchmarking against your own workload if latency is a hard constraint.

Intelligence Evaluations

Under Artificial Analysis’s current Intelligence Index methodology (v5.0), MiMo-V2.6-Pro scores 30 and ranks #3 of 21 models in the “Large” (>150B parameter) size class, where the median is 23. Because the index is periodically rescaled, we’re intentionally not reproducing older cross-model comparison tables here — a score pulled from a previous index version isn’t comparable to a score under the current one unless both were measured on the same methodology.

Professional and Knowledge Benchmarks

Performance on professional and business-relevant tasks is reflected in two Xiaomi-reported figures:

• GDPVal 2.1 (AA, Elo): 1,673, versus Claude Opus 5 at 1,708
• JobBench: 62.0

General knowledge retrieval (AA-Omniscience) results were not independently verified for this guide and are omitted here rather than restated from the earlier draft.

Capability Indices

Capability	MiMo-V2.6-Pro Support
Tool Calling	Yes — see the function calling guide
Reasoning	Yes
Structured Output	Yes (varies by provider)
Multimodal Input	Text, Image, Audio, Video
Temperature Control	Yes

MiMo-V2.6-Pro: Provider Selection Criteria

Buyer Criterion	Model Facts	Why DeepInfra Is a Strong Option
Open-weights friendliness	Open weights, MIT license, weights on Hugging Face	DeepInfra specializes in hosted open-weights consumption, reducing operational burden versus self-hosting while keeping flexibility
Long-context readiness	1M-token context window	DeepInfra provides a production API layer for large-context workloads with rate limits, reliability, and simple integration
Multimodal input support	Inputs: text, image, speech, video; output: text	Consolidates multimodal inference behind a single provider integration pattern
Cost sensitivity	$0.435/M input, $0.87/M output; 99% cache discount; $0.26 per Intelligence Index task	DeepInfra optimizes price/performance for open models without GPU provisioning overhead
Throughput & latency tradeoffs	53.4 t/s output; 3.16s TTFT (both below class median)	Strong infrastructure (routing, hardware availability, scaling) can help offset the model’s baseline speed
Developer experience & portability	Listed across 27+ aggregated providers; 5 on OpenRouter; 4 independently benchmarked by Artificial Analysis	An API-provider approach offers fast onboarding and the ability to switch providers for open models
Self-hosting fallback	Weights available on Hugging Face; MoE: ~1T total / 42B active	Start hosted with DeepInfra and retain a credible self-host path if requirements change

The MiMo V2.6 Model Family

Model	Architecture	Total Params	Active Params	Context	Best For
MiMo V2.6 Pro	Sparse MoE	1.02T	42B	1M	Demanding, long-horizon agentic work
MiMo V2.6 Flash	Sparse MoE	309B	15B	1M	Best balance of intelligence, speed, and cost
MiMo V2.6 Pro UltraSpeed	Same Pro checkpoint	~1T	42B	1M	Pro-level quality when output speed matters
MiMo V2.6 Distill-Qwen-9B	Dense (SFT)	9B	9B	—	Single-GPU and open agentic-RL research

Frequently Asked Questions

Which provider offers the best intelligence vs. cost ratio?

Among the providers DeepInfra tracks, cost per Intelligence Index task is the clearest apples-to-apples comparison: MiMo-V2.6-Pro currently costs about $0.26 per task, which is competitive against several proprietary frontier models while scoring 30 on Artificial Analysis’s current Intelligence Index — the #3 position in the >150B parameter class.

What is the difference between total and active parameters?

MiMo-V2.6-Pro has 1.02 trillion total parameters but activates only 42 billion per forward pass. This Mixture-of-Experts design gives the model large capacity for knowledge storage while keeping inference costs manageable — you are not paying to run the full trillion parameters on every token.

How does prompt caching affect LLM API pricing?

MiMo-V2.6-Pro offers a 99% cache discount, reducing cached input cost from $0.435/M to $0.0036/M tokens. DeepInfra’s prompt caching documentation covers how cache hits are detected and billed. For applications with repeated context — RAG systems, multi-turn conversations, batch processing — this can cut costs substantially.

Can I run MiMo-V2.6-Pro locally?

The weights are public, but at 1.02T total parameters (42B active), a Q4-class build needs roughly 612 GB of VRAM — beyond any consumer machine. Until smaller official builds are released, API access or a multi-GPU server is the practical path.

How does MiMo-V2.6-Pro compare to other open-weight models?

MiMo-V2.6-Pro is one of the higher-scoring open-weights models currently tracked by Artificial Analysis, though it is not the single top-ranked one — it sits #3 of 21 in the >150B parameter class. Specific head-to-head comparisons against other open models (for example, against Kimi K3) weren’t independently verified for this guide, so we’ve left those out rather than repeat an unverified figure.

What modalities does MiMo-V2.6-Pro support?

MiMo-V2.6-Pro is “natively omnimodal,” accepting text, image, video, and audio as input, with text output. This is built into the architecture via dedicated vision (681M params) and audio (308M params) encoders.

What is the recommended configuration for MiMo-V2.6-Pro?

Xiaomi recommends temperature=1.0 and top_p=0.95 for optimal results.

Conclusion

MiMo-V2.6-Pro is a credible entry in the open-weights LLM landscape: a strong, if not table-topping, Intelligence Index score, aggressive pricing, and full MIT-licensed weights.

Key Takeaways

• Strong, not top-ranked: Scores 30 on the current Artificial Analysis Intelligence Index — #3 of 21 in the >150B parameter class, well above the class median of 23.
• Cost-efficient: At roughly $0.26 per Intelligence Index task, it’s priced well below several proprietary frontier models on a cost-per-task basis.
• Fully open: MIT license with weights on Hugging Face enables commercial use, fine-tuning, and self-hosting (with sufficient hardware).
• Multimodal native: Handles text, image, video, and audio input with a 1M-token context window.
• Trade-offs: Slower than the class median (53.4 vs. 73.9 tokens/sec) and higher latency (3.16s vs. 1.67s TTFT).

Who Should Use MiMo-V2.6-Pro

• Teams running high-volume agentic and coding workloads where cost matters
• Organizations requiring open weights for compliance, customization, or self-hosting options
• Developers building multimodal applications with long-context requirements
• Anyone seeking strong, cost-efficient performance without frontier-model pricing

For production deployments, providers like DeepInfra offer hosted access that balances the flexibility of open weights with the convenience of managed infrastructure. Start with the quickstart guide to get your first call running, and keep a credible path to self-hosting if requirements evolve.

The gap between open and proprietary models continues to narrow, and MiMo-V2.6-Pro is a clear data point in that direction — a strong, cost-efficient open-weights model, even if not the single highest-scoring one on the current Intelligence Index. For a look at how it stacks up against another recent agentic release, see our Kimi K3 vs. Claude Opus 4.8 vs. GPT-5.6 Sol comparison.