Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | DeepSeek V4 Flash (Reasoning, Max Effort) | MiMo-V2.5 |
|---|---|---|
| Input ($/M tokens) | $0.14 | $0.14 |
| Output ($/M tokens) | $0.28 | $0.28 |
Verdict. DeepSeek V4 Flash (Reasoning, Max Effort) wins the overall benchmark matchup 5–2 across 7 overlapping categories, but raw benchmark score is only one input to the decision.
Pricing. Both models sit in the budget bracket for output-token pricing. At 1.0× the per-million-token cost, MiMo-V2.5 is meaningfully cheaper if your traffic is output-heavy (long completions, document generation, agent loops). MiMo-V2.5 makes more sense when output volume is low and absolute reasoning quality justifies the premium.
Strengths. DeepSeek V4 Flash (Reasoning, Max Effort) is strongest on GPQA Diamond (89%), IFBench (79%), Coding Index (56.2). MiMo-V2.5 leads on GPQA Diamond (85%), IFBench (67%), Coding Index (56.8).
Speed. On throughput, DeepSeek V4 Flash (Reasoning, Max Effort) generates tokens at 111 tok/s versus 81 tok/s — about 27% faster. On time-to-first-token, DeepSeek V4 Flash (Reasoning, Max Effort) responds in 922ms vs 1901ms, which matters most for chat-style UIs.
Provider. DeepSeek and Xiaomi sell to overlapping but distinct developer audiences: DeepSeek 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.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): DeepSeek V4 Flash (Reasoning, Max Effort) costs $8.40 ($101/year); MiMo-V2.5 costs $8.40 ($101/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): DeepSeek V4 Flash (Reasoning, Max Effort) ≈ $1.26/run, MiMo-V2.5 ≈ $1.26/run. At agent/realtime scale (200M input / 100M output per million requests): DeepSeek V4 Flash (Reasoning, Max Effort) ≈ $56/run, MiMo-V2.5 ≈ $56/run.
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.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
Both models have similar pricing. Check the detailed breakdown above for input vs output token costs.
DeepSeek V4 Flash (Reasoning, Max Effort) wins 5 out of 12 benchmarks compared to 2 for MiMo-V2.5. See the detailed benchmark chart above for per-category results.
DeepSeek V4 Flash (Reasoning, Max Effort) generates tokens faster at 111 tok/s vs 81 tok/s. DeepSeek V4 Flash (Reasoning, Max Effort) also has lower time-to-first-token (0.92s vs 1.90s).
Choose based on your priorities: both are similarly priced, DeepSeek V4 Flash (Reasoning, Max Effort) for stronger benchmark performance, and DeepSeek V4 Flash (Reasoning, Max Effort) for faster generation. For latency-sensitive apps, check the TTFT comparison above.