Xiaomi’s MiMo V2.5 Pro and Google’s Gemini 3.1 Pro both sit near the top of the coding agent leaderboard, but they get there in very different ways. V2.5 Pro burns 40-60% fewer tokens than its competitors while matching or beating them on agentic benchmarks. Gemini 3.1 Pro leans on Google’s ecosystem, a generous free tier, and native multimodal capabilities. Both offer 1M token context windows.
This comparison covers benchmarks, pricing, ecosystem, and real-world differences so you can pick the right model. For deeper dives, see our MiMo V2.5 Pro complete guide and Gemini CLI complete guide.
Architecture overview
These two models take fundamentally different approaches to design and distribution.
| Feature | MiMo V2.5 Pro | Gemini 3.1 Pro |
|---|---|---|
| Developer | Xiaomi | Google DeepMind |
| Total parameters | 1T+ | Undisclosed |
| Active parameters | 42B per token | Undisclosed |
| Architecture | Mixture of Experts (MoE) | Dense (likely) |
| Context window | 1M tokens | 1M tokens |
| Attention | Hybrid (dense + sparse) | Undisclosed |
| License | Open-source (coming) | Proprietary |
| Open weights | Planned | No |
| Primary focus | Long-horizon agentic coding | General-purpose + multimodal |
V2.5 Pro uses a sparse MoE architecture where only 42B of its 1T+ parameters activate per token. This keeps inference costs low while delivering frontier-level reasoning. The hybrid attention mechanism mixes dense and sparse patterns, which helps maintain quality across very long sessions.
Gemini 3.1 Pro’s architecture details remain undisclosed. It is likely a dense transformer with native multimodal support. Both models share the same 1M token context window, which removes context length as a differentiator.
Benchmark comparison
Here is how the two models compare across coding, agentic, and reasoning benchmarks. Data sourced from official reports and leaderboard submissions.
| Benchmark | MiMo V2.5 Pro | Gemini 3.1 Pro | Delta |
|---|---|---|---|
| SWE-bench Pro | 57.2% | 54.2% | V2.5 Pro +3.0 |
| ClawEval Pass^3 | 64% (~70K tokens) | ~58% (~140K tokens) | V2.5 Pro +6.0 |
| Token efficiency (ClawEval) | ~70K per trajectory | ~140K per trajectory | V2.5 Pro 2x better |
| AIME 2026 | Strong | 98.3% | Gemini leads |
| GPQA-Diamond | Strong | 94.3% | Gemini leads |
| MMMU-Pro | N/A (text-only) | 83.0% | Gemini (multimodal) |
V2.5 Pro wins the benchmarks that matter most for agentic coding. Its 57.2% on SWE-bench Pro beats Gemini’s 54.2% by 3 points. On ClawEval, V2.5 Pro scores 64% while using roughly half the tokens Gemini needs. That token efficiency gap is the headline story.
Gemini 3.1 Pro leads on pure reasoning benchmarks like GPQA-Diamond and AIME 2026, and it dominates multimodal tasks where V2.5 Pro does not compete. If your workflow involves image understanding or mixed-media inputs, Gemini has a clear advantage.
For a broader comparison across all frontier models, see our AI model comparison.
Token efficiency
This is where V2.5 Pro separates itself from every other model on the market, not just Gemini.
On ClawEval, V2.5 Pro completes complex agentic tasks using approximately 70K tokens per trajectory. Gemini 3.1 Pro needs around 140K tokens for comparable tasks. That is a 2x difference. Over hundreds of agent runs, this translates directly into lower costs and faster completion times.
Xiaomi calls this “harness awareness.” V2.5 Pro understands the tool-calling environment it operates in and actively manages its own context. Instead of blindly consuming tokens until the window fills up, it makes strategic decisions about what to keep, what to summarize, and when to offload information. Most models leave this entirely to the harness (Claude Code, OpenCode, etc.). V2.5 Pro participates in that management.
The practical impact: V2.5 Pro sustained 1,000+ tool calls in a single session during Xiaomi’s demos. It built a complete SysY compiler in Rust (672 tool calls, 233/233 tests passing) and an 8,192-line video editor (1,868 tool calls). These are multi-hour engineering sessions where token efficiency compounds.
Pricing
| Model | Input (per 1M tokens) | Output (per 1M tokens) | Free tier |
|---|---|---|---|
| MiMo V2.5 Pro | ~$1.00 | ~$3.00 | Limited |
| Gemini 3.1 Pro | Varies by tier | Varies by tier | Yes (Gemini CLI, AI Studio) |
V2.5 Pro’s API pricing sits around $1.00 input / $3.00 output per million tokens. Combined with its 2x token efficiency advantage, the effective cost per task is roughly 3-4x cheaper than Gemini for agentic workflows.
Gemini 3.1 Pro pricing varies depending on whether you use the API directly, AI Studio, or Google Cloud. The free tier through Gemini CLI and AI Studio is generous enough for individual developers and small teams. For production workloads, costs scale with usage.
If you are running agentic workflows at scale, V2.5 Pro’s combination of low per-token pricing and fewer tokens per task makes it the clear cost winner. If you are experimenting or building prototypes, Gemini’s free tier removes the cost question entirely.
Ecosystem and tooling
The ecosystem story is where these models diverge most.
MiMo V2.5 Pro
V2.5 Pro works with third-party coding agents. You can use it through Claude Code (via API configuration), OpenCode, and other tools that support OpenAI-compatible endpoints. Xiaomi has announced plans to open-source the weights, which would make self-hosting possible. The model is also available through OpenRouter and other aggregators.
The tradeoff: there is no first-party CLI or IDE integration from Xiaomi. You are relying on third-party tooling to build your workflow. For a setup walkthrough, see our MiMo V2.5 Pro complete guide.
Gemini 3.1 Pro
Gemini 3.1 Pro has the full weight of Google’s ecosystem behind it. Gemini CLI provides a terminal-based coding agent experience. AI Studio offers a web-based playground. Google Cloud integration means you can use Gemini in Vertex AI pipelines, Cloud Functions, and other GCP services. The model also supports native multimodal input, including images, audio, and video.
If you already use Google tools, Gemini slots in with minimal friction. The ecosystem advantage compounds over time as Google adds more integrations.
Key differences at a glance
Where MiMo V2.5 Pro wins:
- 57.2% SWE-bench Pro vs 54.2% (agentic coding)
- 40-60% fewer tokens per task (2x efficiency)
- Lower effective cost for agentic workflows
- 1,000+ tool call sessions without degradation
- Open-source weights coming
- Harness-aware context management
Where Gemini 3.1 Pro wins:
- Google ecosystem integration (CLI, AI Studio, Cloud)
- Stronger reasoning benchmarks (GPQA-Diamond, AIME 2026)
- Native multimodal support (images, audio, video)
- Generous free tier for experimentation
- First-party CLI and IDE tooling
- Established enterprise support
Verdict
V2.5 Pro and Gemini 3.1 Pro are both strong choices for coding agents, but they serve different priorities.
Pick MiMo V2.5 Pro if you care about cost efficiency and agentic coding performance. The 2x token efficiency advantage is not a minor optimization. It cuts your API bills in half and speeds up task completion. If you are running complex, multi-step engineering workflows at scale, V2.5 Pro delivers more work per dollar than any other frontier model right now.
Pick Gemini 3.1 Pro if you value ecosystem integration, multimodal capabilities, or need a free tier to get started. Google’s tooling is polished and well-documented. If your workflow involves image understanding, mixed-media inputs, or tight integration with GCP services, Gemini is the better fit.
For teams that can afford to use both: route agentic coding tasks to V2.5 Pro for cost savings, and use Gemini for multimodal work and tasks that benefit from Google’s ecosystem. That is the optimal split.
For more model comparisons, see Kimi K2.6 vs Gemini 3.1 Pro and our full AI model comparison.
FAQ
Is MiMo V2.5 Pro open-source?
Not yet. Xiaomi has announced plans to open-source the weights, but as of April 2026, V2.5 Pro is available through API access only. When the weights are released, you will be able to self-host the model on your own infrastructure. The MoE architecture with 42B active parameters makes it more feasible to run than a dense model of equivalent capability.
Can I use Gemini 3.1 Pro for free?
Yes. Google offers a free tier through Gemini CLI and AI Studio with rate limits. This is sufficient for personal projects, prototyping, and experimentation. Production use at scale requires a paid API plan or Google Cloud billing.
Which model should I use for long coding sessions?
MiMo V2.5 Pro is specifically designed for long-horizon agentic tasks. It sustained 1,000+ tool calls in Xiaomi’s demos without quality degradation. Its harness awareness and token efficiency mean it can work longer on the same context budget. Gemini 3.1 Pro handles long sessions well too, thanks to its 1M token context window, but it consumes roughly twice as many tokens to complete comparable tasks.