Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Llama 2 Chat 70B | MiMo-V2.5-Pro |
|---|---|---|
| Input ($/M tokens) | — | $0.43 |
| Output ($/M tokens) | — | $0.87 |
Verdict. MiMo-V2.5-Pro takes the aggregate benchmark matchup 2–0 across 2 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Pricing varies significantly between these models — check the table above for the exact per-token rates. Many production workloads actually surface input-token cost (retrieval-augmented prompts, code-context windows), so factor both directions.
Strengths. Llama 2 Chat 70B is strongest on Intelligence Index (2.6). MiMo-V2.5-Pro leads on Coding Index (60.2), Intelligence Index (42.9).
Speed. Speed data is incomplete for this pair; benchmark and price should decide.
Provider. Meta and Xiaomi sell to overlapping but distinct developer audiences: Meta tends to ship frontier reasoning models with premium positioning, while Xiaomi often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Data from Artificial Analysis API — 12 benchmarks
MiMo-V2.5-Pro is cheaper overall. Its blended price (3:1 input/output ratio) is $0.54/M tokens vs $—/M for Llama 2 Chat 70B.
MiMo-V2.5-Pro wins 2 out of 12 benchmarks compared to 0 for Llama 2 Chat 70B. See the detailed benchmark chart above for per-category results.
MiMo-V2.5-Pro generates tokens faster at 59 tok/s vs — tok/s. However, MiMo-V2.5-Pro has lower time-to-first-token (3.18s vs —s).
Choose based on your priorities: MiMo-V2.5-Pro for lower cost, MiMo-V2.5-Pro for stronger benchmark performance, and MiMo-V2.5-Pro for faster generation. For latency-sensitive apps, check the TTFT comparison above.