Gemma 4 26B A4B vs MiMo-V2-Flash
Head-to-head specifications
| Metric | Gemma 4 26B A4B | MiMo-V2-Flash | Difference |
|---|---|---|---|
| Intelligence Index | 25.0 | 28.0 | -10.7% |
| Coding Index | 39.3 | 49.8 | -21.1% |
| Agentic Index | 11.0 | 12.0 | — |
| Context window | 400K tokens | 272K tokens | — |
| Blended price ($/1M tokens) | $0.13 | $0.12 | +8.3% |
| Access | Open weights | Open weights | — |
- MiMo-V2-Flash leads overall capability (Intelligence Index 28.0 vs 25.0).
- MiMo-V2-Flash is the cheaper model to run at $0.12/1M blended tokens — about 1.1× cheaper.
- Gemma 4 26B A4B offers the larger context window (400K tokens), useful for long documents and codebases.
Verdict: Gemma 4 26B A4B or MiMo-V2-Flash?
Gemma 4 26B A4B advantages
- Context window (+32%)
MiMo-V2-Flash advantages
- General intelligence (+11%)
- Coding ability (+21%)
- Agentic task performance (+8%)
- Affordability (+8%)
Which should you choose?
- Choose the Gemma 4 26B A4B if you work with long documents or large codebases.
- Choose the MiMo-V2-Flash if you need the strongest overall reasoning and accuracy.
Value for money
MiMo-V2-Flash offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Gemma 4 26B A4B vs MiMo-V2-Flash: which should you choose?
Gemma 4 26B A4B — Google text model with an Intelligence Index of 25, a 400K-token context window and a blended price of $0.13/1M tokens (open weights).
MiMo-V2-Flash — Xiaomi multimodal model with an Intelligence Index of 28, a 272K-token context window and a blended price of $0.12/1M tokens (open weights).
Gemma 4 26B A4B vs MiMo-V2-Flash: MiMo-V2-Flash scores higher on the Intelligence Index. MiMo-V2-Flash leads overall capability (Intelligence Index 28.0 vs 25.0). MiMo-V2-Flash is the cheaper model to run at $0.12/1M blended tokens — about 1.1× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the MiMo-V2-Flash scores 28.0 versus 25.0. For software development, the Coding Index puts MiMo-V2-Flash ahead (49.8 vs 39.3). On agentic, multi-step tool-use tasks, MiMo-V2-Flash measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The Gemma 4 26B A4B accepts up to 400K tokens per request, which sets how much documentation, transcript or code it can reason over at once.
Pricing and access
At blended per-token rates, MiMo-V2-Flash is the cheaper model to run ($0.12 vs $0.13 per 1M tokens). Gemma 4 26B A4B is open weights and MiMo-V2-Flash is open weights. Open-weight models can be self-hosted, trading per-call cost for infrastructure you manage; for production also weigh rate limits, throughput and data-residency requirements.
The verdict
Both are credible choices in the ai model comparison space; the specification table above lays out every metric so you can weigh the trade-offs that matter to you. Pick the one whose strengths line up with how you will actually use it.
Frequently asked questions
Is the Gemma 4 26B A4B better than the MiMo-V2-Flash?
MiMo-V2-Flash is the clearly stronger overall choice, winning most of the dimensions that matter. MiMo-V2-Flash leads overall capability (Intelligence Index 28.0 vs 25.0).
What is the main difference between the Gemma 4 26B A4B and the MiMo-V2-Flash?
MiMo-V2-Flash leads overall capability (Intelligence Index 28.0 vs 25.0). MiMo-V2-Flash is the cheaper model to run at $0.12/1M blended tokens — about 1.1× cheaper.
Which is better value?
MiMo-V2-Flash offers more intelligence per dollar (1.2× the Intelligence-Index-per-cost of the alternative), making it the stronger value for high-volume use. It is also open-weight, so self-hosting can reduce costs further at scale.
Which should I choose?
Choose the Gemma 4 26B A4B if you work with long documents or large codebases. Choose the MiMo-V2-Flash if you need the strongest overall reasoning and accuracy.
Methodology
Large language models are compared on independent leaderboard metrics: an Intelligence Index (a composite of reasoning and knowledge evaluations), Coding and Agentic indices where measured, community arena Elo, maximum context window, a blended API price per million tokens (weighted across cache-hit, input and output rates), and measured output speed in tokens per second. Where a model ships multiple reasoning-effort variants, we report its strongest variant. Benchmarks capture only part of real-world quality, which also depends on tool use, latency, safety and task fit — and this space moves quickly, so figures reflect the leaderboard snapshot on the page date.