LLM Comparison
Llama 4 Maverick vs MiMo-V2.6-Flash
Side-by-side specs, pricing & capabilities · Updated October 2026
Price vs Intelligence
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2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 24 62.9 | 57 273 |
| Family | Llama | MiMo |
| Status | Current | Current |
| Release Date | Apr 2025 | Sep 2026 |
| Context Window | 1.0M tokens | 1.0M tokens |
| Input Price | $0.15/M tokens | $0.14/M tokens |
| Output Price | $0.60/M tokens | $0.28/M tokens |
| Pricing Notes | — | USD per 1M tokens for Xiaomi MiMo pay-as-you-go API access. Cache-hit input is $0.0028 per 1M tokens. |
| Capabilities | textvisioncode | reasoningmultimodalcodingtool-useweb-searchstructured-outputcomputer-usecontext-cachingopen-weightslong-context |
| Max Output | 16K tokens | 128K tokens |
| API Identifier | meta-llama/llama-4-maverick | mimo-v2.6-flash |
| Benchmarks | ||
| MMLU | 85.5meta | — |
| MMLU Pro | 80.5meta | — |
| GPQA Diamond | 69.8meta | — |
| MATH | 61.2meta | — |
| LiveCodeBench (10/01/2024-02/01/2025) | 43.4meta | — |
| MMMU | 73.4meta | — |
| Artificial Analysis Intelligence Index v4.3 | 9.299artificial-analysis | — |
| SciCode | 31.713artificial-analysis | — |
| Terminal-Bench 2.1 | 7.8652artificial-analysis | 87.6xiaomi-mimo |
| AIME 2025 | 19.3333artificial-analysis | — |
| DeepSWE v1.1 | — | 67.9xiaomi-mimo |
| AutomationBench v1.0.6 | — | 52.3xiaomi-mimo |
| Toolathlon-Verified | — | 73.6xiaomi-mimo |
| Terminal-Bench 4.0 | — | 28.8xiaomi-mimo |
| Artificial Analysis Intelligence Index v4.3.2 | — | 37.8843590141754artificial-analysis |
| View Llama 4 Maverick | View MiMo-V2.6-Flash | |
Cost Calculator
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| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| MiMo-V2.6-FlashCheapest | $0.14 | $0.14 | $0.28 | — |
| Llama 4 Maverick | $0.15 | $0.30 | $0.45 | +61% |
Meta
Llama 4 Maverick
Llama 4 Maverick is a multimodal llm from Meta. Supports up to 1,048,576 token context window. Achieves 88.4% on MMLU. Available from $0.15/M input tokens.
Xiaomi
MiMo-V2.6-Flash
MiMo-V2.6-Flash is Xiaomi's MIT-licensed, open-weight multimodal reasoning model optimized for high-frequency and large-scale professional workloads. It supports text, image, video, and audio input, a one-million-token context window, 128K-token output, tool use, web search, structured output, and agent workflows.
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