Gemma 4 26B A4B vs MiMo-V2.5
Head-to-head specifications
| Metric | Gemma 4 26B A4B | MiMo-V2.5 | Difference |
|---|---|---|---|
| Intelligence Index | 25.0 | 37.0 | -32.4% |
| Coding Index | 39.3 | 56.8 | -30.8% |
| Agentic Index | 11.0 | 23.7 | — |
| Context window | 400K tokens | 1M tokens | — |
| Blended price ($/1M tokens) | $0.13 | $0.06 | +116.7% |
| Output speed (tokens/s) | 54 | 83 | -34.9% |
| Access | Open weights | Open weights | — |
- MiMo-V2.5 leads overall capability (Intelligence Index 37.0 vs 25.0).
- MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 2.2× cheaper.
- MiMo-V2.5 offers the larger context window (1M tokens), useful for long documents and codebases.
Verdict: Gemma 4 26B A4B or MiMo-V2.5?
Gemma 4 26B A4B advantages
- No decisive advantage on the tracked metrics.
MiMo-V2.5 advantages
- General intelligence (+32%)
- Coding ability (+31%)
- Agentic task performance (+54%)
- Context window (+60%)
- Affordability (+54%)
- Output speed (+35%)
Which should you choose?
- Choose the MiMo-V2.5 if you need the strongest overall reasoning and accuracy.
Value for money
MiMo-V2.5 offers more intelligence per dollar (3.2× 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-V2.5: 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-V2.5 — Xiaomi multimodal model with an Intelligence Index of 37, a 1M-token context window and a blended price of $0.06/1M tokens (open weights).
Gemma 4 26B A4B vs MiMo-V2.5: MiMo-V2.5 scores higher on the Intelligence Index. MiMo-V2.5 leads overall capability (Intelligence Index 37.0 vs 25.0). MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 2.2× cheaper.
Capability: intelligence, coding and agentic work
On the composite Intelligence Index the MiMo-V2.5 scores 37.0 versus 25.0. For software development, the Coding Index puts MiMo-V2.5 ahead (56.8 vs 39.3). On agentic, multi-step tool-use tasks, MiMo-V2.5 measures stronger. Composite indices summarize many evaluations, but always test on your own workload before committing.
Context window and speed
The MiMo-V2.5 accepts up to 1 million tokens per request, which sets how much documentation, transcript or code it can reason over at once. In measured throughput, MiMo-V2.5 generates faster (83 vs 54 tokens/s), which matters for interactive apps and high-volume pipelines.
Pricing and access
At blended per-token rates, MiMo-V2.5 is the cheaper model to run ($0.06 vs $0.13 per 1M tokens). Gemma 4 26B A4B is open weights and MiMo-V2.5 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-V2.5?
MiMo-V2.5 is the clearly stronger overall choice, winning most of the dimensions that matter. MiMo-V2.5 leads overall capability (Intelligence Index 37.0 vs 25.0).
What is the main difference between the Gemma 4 26B A4B and the MiMo-V2.5?
MiMo-V2.5 leads overall capability (Intelligence Index 37.0 vs 25.0). MiMo-V2.5 is the cheaper model to run at $0.06/1M blended tokens — about 2.2× cheaper.
Which is better value?
MiMo-V2.5 offers more intelligence per dollar (3.2× 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 MiMo-V2.5 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.