Models / mimo-v2-6-flash
MiMo V2.6 Flash
Xiaomi's model, with open weights. Forcebench has run it in 2 configurations on the API and Local boards, plus 1 on the lite subset. API is models reached through a hosted service; Local, models served on Forcebench’s own hardware.
On the API board
Models reached through a hosted service. MiMo V2.6 Flash is ranked by its one configuration: the 21st-highest score of 26 models, not clearly different from 4 other models. Scores are within 3 attempts (c@3) with 95% intervals; each configuration is compared with the highest-scoring one by whether their 95% intervals overlap. Every configuration on the board → Cost and speed →
| Configuration | Rank all configs | Within 3 attempts c@3 · 95% CI | Tokens mean · median · p90 | No answer | Latency median per task, every attempt counted | Cost per task | Cost per solved task |
|---|---|---|---|---|---|---|---|
| OpenRouter · FP8 · thinking onhighest score | 34 | 55%95% CI 50–60 | 13k6.7k · 33k | 23% | 244 s | $0.0085 | $0.015 |
- 95% interval
- API board median
Show the numbers
| Suite | c@3 95% CI | API median | Tasks |
|---|---|---|---|
| Apex | 90%95% CI 70–97 | 90% | 20 |
| Salesforce APIs | 44%95% CI 25–66 | 72% | 18 |
| CI/CD | 65%95% CI 41–83 | 88% | 17 |
| Salesforce CLI | 55%95% CI 34–74 | 90% | 20 |
| Salesforce docs | 24%95% CI 10–47 | 29% | 17 |
| fflib Enterprise Patterns | 22%95% CI 9–45 | 61% | 18 |
| Flow | 17%95% CI 6–39 | 69% | 18 |
| Governor limits & pushback | 68%95% CI 46–85 | 89% | 19 |
| Lightning Web Components | 94%95% CI 74–99 | 100% | 18 |
| Nonprofit Success Pack | 56%95% CI 34–75 | 81% | 18 |
| Packaging | 44%95% CI 25–66 | 69% | 18 |
| Permissions & access | 73%95% CI 48–89 | 87% | 15 |
| Scratch org definitions | 11%95% CI 3–33 | 50% | 18 |
| SOQL | 100%95% CI 84–100 | 100% | 20 |
| Trigger Actions Framework | 56%95% CI 34–75 | 86% | 18 |
On the Local board
Models served on Forcebench’s own hardware. MiMo V2.6 Flash is ranked by its one configuration: the fifth-highest score of 9 models, not clearly different from 3 other models. Scores are within 3 attempts (c@3) with 95% intervals; each configuration is compared with the highest-scoring one by whether their 95% intervals overlap. Every configuration on the board →
| Configuration | Rank all configs | Within 3 attempts c@3 · 95% CI | Tokens mean · median · p90 | No answer | Latency median per task, every attempt counted |
|---|---|---|---|---|---|
| FP8 + MXFP4 experts · SGLang · thinking on · 2× NVIDIA DGX Spark (GB10, 128 GB each)highest score | 8 | 51%95% CI 46–56 | 14k7.1k · 33k | 25% | 1619 s |
Latency depends on the hardware: compare it only between configurations on the same hardware.
- 95% interval
- Local board median
Show the numbers
| Suite | c@3 95% CI | Local median | Tasks |
|---|---|---|---|
| Apex | 85%95% CI 64–95 | 70% | 20 |
| Salesforce APIs | 39%95% CI 20–61 | 44% | 18 |
| CI/CD | 35%95% CI 17–59 | 53% | 17 |
| Salesforce CLI | 45%95% CI 26–66 | 45% | 20 |
| Salesforce docs | 12%95% CI 3–34 | 12% | 17 |
| fflib Enterprise Patterns | 44%95% CI 25–66 | 17% | 18 |
| Flow | 17%95% CI 6–39 | 17% | 18 |
| Governor limits & pushback | 74%95% CI 51–88 | 84% | 19 |
| Lightning Web Components | 78%95% CI 55–91 | 89% | 18 |
| Nonprofit Success Pack | 56%95% CI 34–75 | 61% | 18 |
| Packaging | 39%95% CI 20–61 | 39% | 18 |
| Permissions & access | 73%95% CI 48–89 | 60% | 15 |
| Scratch org definitions | 11%95% CI 3–33 | 17% | 18 |
| SOQL | 100%95% CI 84–100 | 90% | 20 |
| Trigger Actions Framework | 56%95% CI 34–75 | 44% | 18 |
In the studies
- Local vs API: where it lands among the configurations of both boards.
- Pushback: whether it pushed back on requests that break at scale.
Private evaluations
Want this for your team?
Azul Labs runs Forcebench privately for two kinds of Salesforce team.
Choosing a model to run yourself?
A private benchmark of your shortlist
Open-weight models at the quantisations and reasoning efforts you could run on infrastructure you control, tested on your kind of Salesforce work, with a written recommendation.
- Private tasks written from your work only ever run on local models; they are never sent to an API.
- Re-testing after any change of model, quantisation or engine is available.
- Typical turnaround is 2–3 weeks; large models take about a day of machine time per configuration.
Building or buying a Salesforce AI tool?
A private evaluation of your tool
Your tool on held-out tasks it has never seen, against API baselines (models reached through a hosted service) and Local ones (served on Forcebench’s own hardware), with a written report on where it holds up and where it fails.
- It can start as a smaller pilot, about 30 held-out tasks with your tool against two baselines, before a full evaluation.
- Repeat evaluations after your releases are available.
- Typical turnaround is 2–3 weeks.
Fixed fee, quoted up front. The quote depends on the number of tasks and the number of models, tools or configurations tested.
Or email leo@azl.au · azl.au