The vendor reports that after 30 RL steps and approximately 750,000 cumulative trajectories, MiMo-V2.6-Flash improved from 48.8 to 65.7 on DeepSWE v1.1; in the official Agent benchmark table, Flash scored 95.1 on CyberGym, 87.6 on Terminal Bench 2.1, and 71.5 on MiMo Visual Coding. All of these are results disclosed on Xiaomi's page, not independent reproductions.
Xiaomi MiMo official documentation · Read evidenceMiMo-V2.6-Flash · Reviews and evidence
Which MiMo-V2.6-Flash conclusions hold up?
Browse public evaluations by topic, source identity, and evidence type. Different versions, tiers, and harnesses are not treated as directly comparable.
This is a third-party source navigator, not a Tabbit test. Use the original source for live metrics; unknown values remain unknown.
Editorial takeaways
Editorial takeaways
The official model card defines XiaomiMiMo/MiMo-V2.6-Flash-RL as the efficiency-balanced checkpoint in the MiMo-V2.6 series and reports its results on code, general Agent, cybersecurity, and visual Agent benchmarks. However, the evaluation hardware, sample sizes, complete harnesses, prompts, and decoding settings have not been disclosed.
Hugging Face · Read evidenceThe official post positions MiMo-V2.6-Flash as an omnimodal model alongside Pro, emphasizing scaled reinforcement learning, coding/general Agent/cybersecurity/visual Agent capabilities, and open reproduction; the attached image gives Flash's individual benchmark scores but does not disclose the sample size, complete harness, hardware, prompts, or decoding parameters。
X (Twitter) · Read evidenceFull reviews and related reading
Selected evidence
Media / benchmarkVendor report
MiMo-V2.6-Flash Official Benchmarks: 30 RL Steps and Agent Results
The vendor reports that after 30 RL steps and approximately 750,000 cumulative trajectories, MiMo-V2.6-Flash improved from 48.8 to 65.7 on DeepSWE v1.1; in the official Agent benchmark table, Flash scored 95.1 on CyberGym, 87.6 on Terminal Bench 2.1, and 71.5 on MiMo Visual Coding. All of these are results disclosed on Xiaomi's page, not independent reproductions.
SourceXiaomi MiMo official documentation
Published2026-09-22
Collected2026-09-22
- Source-specific observation
- The vendor reports that after 30 RL steps and approximately 750,000 cumulative trajectories, MiMo-V2.6-Flash improved from 48.8 to 65.7 on DeepSWE v1.1; in the official Agent benchmark table, Flash scored 95.1 on CyberGym, 87.6 on Terminal Bench 2.1, and 71.5 on MiMo Visual Coding. All of these are results disclosed on Xiaomi's page, not independent reproductions.
- Published conditions
- These results cannot be used to infer performance across all real-world businesses, different prompts, different toolchains, or different inference parameters; the page does not provide the sample size for each benchmark, complete prompts, decoding settings, random seeds, hardware configuration。
Media / benchmarkVendor report
MiMo-V2.6-Flash-RL Hugging Face Official Benchmarks and Deployment Boundaries
The official model card defines XiaomiMiMo/MiMo-V2.6-Flash-RL as the efficiency-balanced checkpoint in the MiMo-V2.6 series and reports its results on code, general Agent, cybersecurity, and visual Agent benchmarks. However, the evaluation hardware, sample sizes, complete harnesses, prompts, and decoding settings have not been disclosed.
SourceHugging Face
PublishedUnknown
Collected2026-09-22
- Source-specific observation
- The official model card defines XiaomiMiMo/MiMo-V2.6-Flash-RL as the efficiency-balanced checkpoint in the MiMo-V2.6 series and reports its results on code, general Agent, cybersecurity, and visual Agent benchmarks. However, the evaluation hardware, sample sizes, complete harnesses, prompts, and decoding settings have not been disclosed.
- Published conditions
- Accuracy, latency, throughput, cost, and production stability for unlisted tasks or under different harnesses or decoding parameters; cross-model comparisons in the table cannot replace independent testing under matched conditions.
CommunityVendor report
MiMo-V2.6-Flash Official X Release Thread: Flash's Benchmark Positioning and Dual-Model Strategy
The official post positions MiMo-V2.6-Flash as an omnimodal model alongside Pro, emphasizing scaled reinforcement learning, coding/general Agent/cybersecurity/visual Agent capabilities, and open reproduction; the attached image gives Flash's individual benchmark scores but does not disclose the sample size, complete harness, hardware, prompts, or decoding parameters。
SourceX (Twitter)
Published2026-09-21
Collected2026-09-22
- Source-specific observation
- The official post positions MiMo-V2.6-Flash as an omnimodal model alongside Pro, emphasizing scaled reinforcement learning, coding/general Agent/cybersecurity/visual Agent capabilities, and open reproduction; the attached image gives Flash's individual benchmark scores but does not disclose the sample size, complete harness, hardware, prompts, or decoding parameters。
- Published conditions
- Real-world business success rates, latency, throughput, cost, stability, or performance under different toolchains or inference parameters; Pro-exclusive claims must not be attributed to Flash.
Media / benchmarkEditorial analysis
BenchLM: Same-Family Cost and Public Benchmark Comparison of MiMo-V2.6-Flash and Pro
The BenchLM page lists 12 shared public benchmark results and estimated costs for three fixed-token scenarios for MiMo-V2.6-Flash and MiMo-V2.6-Pro: Flash wins one of the 12 results, CyberGym, while Pro wins the other 11; however, neither model is ranked in the current public ranking channels, and the page explicitly does not name an overall quality winner。
SourceBenchLM.ai
Published2026-09-21
Collected2026-09-22
- Source-specific observation
- The BenchLM page lists 12 shared public benchmark results and estimated costs for three fixed-token scenarios for MiMo-V2.6-Flash and MiMo-V2.6-Pro: Flash wins one of the 12 results, CyberGym, while Pro wins the other 11; however, neither model is ranked in the current public ranking channels, and the page explicitly does not name an overall quality winner。
- Published conditions
- Overall quality rankings, unlisted tasks, real-world business success rates, latency, throughput, stability, or performance under different editors, harnesses, reasoning efforts, or prompts. The page also does not provide the sample size, raw outputs, or confidence intervals for BenchLM's independent runs.
All sources
All sources
Media / benchmarkVendor report
MiMo-V2.6-Flash Official Benchmarks: 30 RL Steps and Agent Results
The vendor reports that after 30 RL steps and approximately 750,000 cumulative trajectories, MiMo-V2.6-Flash improved from 48.8 to 65.7 on DeepSWE v1.1; in the official Agent benchmark table, Flash scored 95.1 on CyberGym, 87.6 on Terminal Bench 2.1, and 71.5 on MiMo Visual Coding. All of these are results disclosed on Xiaomi's page, not independent reproductions.
SourceXiaomi MiMo official documentation
Published2026-09-22
Collected2026-09-22
- Source-specific observation
- The vendor reports that after 30 RL steps and approximately 750,000 cumulative trajectories, MiMo-V2.6-Flash improved from 48.8 to 65.7 on DeepSWE v1.1; in the official Agent benchmark table, Flash scored 95.1 on CyberGym, 87.6 on Terminal Bench 2.1, and 71.5 on MiMo Visual Coding. All of these are results disclosed on Xiaomi's page, not independent reproductions.
- Published conditions
- These results cannot be used to infer performance across all real-world businesses, different prompts, different toolchains, or different inference parameters; the page does not provide the sample size for each benchmark, complete prompts, decoding settings, random seeds, hardware configuration。
Media / benchmarkVendor report
MiMo-V2.6-Flash-RL Hugging Face Official Benchmarks and Deployment Boundaries
The official model card defines XiaomiMiMo/MiMo-V2.6-Flash-RL as the efficiency-balanced checkpoint in the MiMo-V2.6 series and reports its results on code, general Agent, cybersecurity, and visual Agent benchmarks. However, the evaluation hardware, sample sizes, complete harnesses, prompts, and decoding settings have not been disclosed.
SourceHugging Face
PublishedUnknown
Collected2026-09-22
- Source-specific observation
- The official model card defines XiaomiMiMo/MiMo-V2.6-Flash-RL as the efficiency-balanced checkpoint in the MiMo-V2.6 series and reports its results on code, general Agent, cybersecurity, and visual Agent benchmarks. However, the evaluation hardware, sample sizes, complete harnesses, prompts, and decoding settings have not been disclosed.
- Published conditions
- Accuracy, latency, throughput, cost, and production stability for unlisted tasks or under different harnesses or decoding parameters; cross-model comparisons in the table cannot replace independent testing under matched conditions.
CommunityVendor report
MiMo-V2.6-Flash Official X Release Thread: Flash's Benchmark Positioning and Dual-Model Strategy
The official post positions MiMo-V2.6-Flash as an omnimodal model alongside Pro, emphasizing scaled reinforcement learning, coding/general Agent/cybersecurity/visual Agent capabilities, and open reproduction; the attached image gives Flash's individual benchmark scores but does not disclose the sample size, complete harness, hardware, prompts, or decoding parameters。
SourceX (Twitter)
Published2026-09-21
Collected2026-09-22
- Source-specific observation
- The official post positions MiMo-V2.6-Flash as an omnimodal model alongside Pro, emphasizing scaled reinforcement learning, coding/general Agent/cybersecurity/visual Agent capabilities, and open reproduction; the attached image gives Flash's individual benchmark scores but does not disclose the sample size, complete harness, hardware, prompts, or decoding parameters。
- Published conditions
- Real-world business success rates, latency, throughput, cost, stability, or performance under different toolchains or inference parameters; Pro-exclusive claims must not be attributed to Flash.
Media / benchmarkEditorial analysis
BenchLM: Same-Family Cost and Public Benchmark Comparison of MiMo-V2.6-Flash and Pro
The BenchLM page lists 12 shared public benchmark results and estimated costs for three fixed-token scenarios for MiMo-V2.6-Flash and MiMo-V2.6-Pro: Flash wins one of the 12 results, CyberGym, while Pro wins the other 11; however, neither model is ranked in the current public ranking channels, and the page explicitly does not name an overall quality winner。
SourceBenchLM.ai
Published2026-09-21
Collected2026-09-22
- Source-specific observation
- The BenchLM page lists 12 shared public benchmark results and estimated costs for three fixed-token scenarios for MiMo-V2.6-Flash and MiMo-V2.6-Pro: Flash wins one of the 12 results, CyberGym, while Pro wins the other 11; however, neither model is ranked in the current public ranking channels, and the page explicitly does not name an overall quality winner。
- Published conditions
- Overall quality rankings, unlisted tasks, real-world business success rates, latency, throughput, stability, or performance under different editors, harnesses, reasoning efforts, or prompts. The page also does not provide the sample size, raw outputs, or confidence intervals for BenchLM's independent runs.
CommunityPersonal experience
MiMo-V2.6-Flash: First-hand Reddit Feedback on Compiler and GC Development
One Reddit user said they had used the model, written exactly as “MiMo V2.6 flash,” for compiler/GC development without encountering any problems and planned to keep using it. However, they provided no task details, environment, prompts, parameters, sample size, or objective metrics, so this can only serve as a personal usability signal.
SourceReddit, r/opencode
Published2026-09-21
Collected2026-09-22
- Source-specific observation
- One Reddit user said they had used the model, written exactly as “MiMo V2.6 flash,” for compiler/GC development without encountering any problems and planned to keep using it. However, they provided no task details, environment, prompts, parameters, sample size, or objective metrics, so this can only serve as a personal usability signal.
- Published conditions
- This does not support inferences about general programming ability, compiler correctness, GC performance, throughput, cost, or large-scale stability, and it should not be extrapolated to Pro, DeepSeek, or other MiMo versions.
MiMo-V2.6-Flash
Compare MiMo-V2.6-Flash in Tabbit
Model access, features, and permissions depend on your current client account.