MiMo-V2.6-Flash

Open Weights • Open Sourcegeneralcodingfastbudgetopen-source

Leaderboard Score

52.4

Scale: 0 - 100

Throughput Speed

185 tps

Context Window

API Pricing / 1M

Free/Open Source

Parameter Scale

Open Weights

Verified AI Radar & Benchmarks•8 Evaluated Metrics

Capability Radar & Benchmark Scores

Multi-Axis Capability Radar

Comparing MiMo-V2.6-Flash against Frontier Top 10 & Open-Source average

0 - 100 Scale

MiMo-V2.6-Flash

Frontier Top 10 Avg

Open-Source Avg

Performance Vectors

Where MiMo-V2.6-Flash Leads

Coding & SWETop 10: 79.1% • OS: 47.6%

67.2%-11.9% vs Top 10

Reasoning & MathTop 10: 74.6% • OS: 46.6%

54.8%-19.8% vs Top 10

Agent & Tool AutomationTop 10: 71.2% • OS: 31.9%

32.5%-38.7% vs Top 10

Terminal & CLI ExecutionTop 10: 80.4% • OS: 47.6%

64.5%-15.9% vs Top 10

MMLU & Knowledge QATop 10: 50% • OS: 86.7%

65.5%+15.5% vs Top 10

Cyber & Security ExploitsTop 10: 87.8% • OS: 80.6%

49.8%-38.0% vs Top 10

All Verified Benchmark Evaluations

Direct Pass@1 and resolution rates across standardized evaluations.

CodingReasoningAgenticMath

Featured Compute Platform

Run Benchmarks on Dedicated AI Clusters

Zero-cold-start inference endpoints with dedicated GPU instances.

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Executive Analysis & Verdict

MiMo-V2.6-Flash by Xiaomi is an ultra-fast, open-weights multimodal model ranking #29 globally (52.4 overall) and #4 on OpenCode, delivering 67.2% SWE-bench coding performance at 185 tps throughput.

Key Strengths & Highlights

•#4 Developer Adoption in OpenCode with 9,486 daily active developers
•High generation speed of 185 tps with sub-220ms time-to-first-token
•67.2% SWE-bench Verified coding capability with open-weights availability

Limitations & Considerations

•Requires multi-GPU VRAM setup for local full-context 1M deployment

Recommended Workflows

Real-time coding assistants, fast interactive autocomplete, agentic tool loops, and high-concurrency API integrations

The Developer Favorite for Real-Time Coding

Xiaomi’s MiMo-V2.6-Flash has surged into the top 4 most widely used models on the OpenCode developer network. Built for speed, it combines 185 tps throughput, a 1M token context window, and 67.2% SWE-bench coding capability with an accessible open-weights license.

Key Specifications

Provider: Xiaomi
License / Weights: Open Source
Context Window: 1,048,576 tokens (1M)
Max Output: 131,072 tokens
Inference Speed: 185 tps
API Pricing: $0.14 / 1M input tokens ($0.28 / 1M output tokens, $0.0028 cache read)

Benchmark Highlights

Overall Score: 52.4 / 100 (Global Rank #29)
SWE-bench Verified: 67.2%
Terminal-Bench: 64.5%
GPQA Diamond: 54.8%
MATH-500: 62.0%
LMSYS Arena Elo: 1,560
OpenCode Rank: #4 Globally (9,486 active developers)

Frequently Asked Questions About MiMo-V2.6-Flash

What is MiMo-V2.6-Flash and who developed it?↓

MiMo-V2.6-Flash is a open source large language model developed by Xiaomi. It ranks #29 on the RankLLMs composite leaderboard, ahead of 68% of the 85 models we track, with an overall score of 52.4 / 100.

What context window and inference speed does MiMo-V2.6-Flash offer?↓

MiMo-V2.6-Flash supports a NaNK-token input context window and generates about 185 tps tokens per second in our reference measurements and suitable for extended reasoning sessions and multi-turn agent workflows..

Can I self-host MiMo-V2.6-Flash?↓

Yes. MiMo-V2.6-Flash is released with Open Source licensing, so it can be self-hosted on private infrastructure with no per-token API cost. Check the specifications above for the memory footprint and quantization headroom to plan your hardware.

What is MiMo-V2.6-Flash best at?↓

MiMo-V2.6-Flash is strongest in #4 Developer Adoption in OpenCode with 9,486 daily active developers; High generation speed of 185 tps with sub-220ms time-to-first-token; 67.2% SWE-bench Verified coding capability with open-weights availability. Full pillar-by-pillar numbers are in the benchmark tables above.

What should you watch out for with MiMo-V2.6-Flash?↓

Known trade-offs include: Requires multi-GPU VRAM setup for local full-context 1M deployment. Weigh these against the strengths above for your specific workload.