Published: July 20, 2026 · Last reviewed: July 20, 2026
Xiaomi MiMo vs GPT-4o mini — Open-Weights vs Closed-Source, Side-by-Side
MiMo-V2.5 standard and GPT-4o mini sit at almost the same list price — $0.14 vs $0.15 per 1M input, $0.28 vs $0.60 per 1M output. MiMo is ~2× cheaper on output. But the differentiator isn't list price: it's cache-hit pricing. MiMo-V2.5 standard charges $0.0028/M on cached input — a 50× reduction vs its own list rate. For workloads with repeated prefixes (chat history, RAG, code review, agent memory), the effective MiMo cost is 50-120× lower than GPT-4o mini. Add 1M context (vs GPT-4o mini's 128k), MIT licensing, and a more recent training cutoff, and MiMo-V2.5 standard is the default answer unless you specifically need GPT-4o mini's stronger vision or OpenAI-native integrations.
1. Pricing (per 1M tokens, July 2026 list rates)
| Model | List input | Cache-hit input | List output | Context | License |
|---|---|---|---|---|---|
| MiMo-V2.5 standard | $0.14 | $0.0028 (-98%) | $0.28 | 1M | MIT |
| MiMo-V2.5-Pro | $0.42 | $0.0035 (-99%) | $0.83 | 1M | MIT |
| GPT-4o mini | $0.15 | — (no cache tier) | $0.60 | 128k | Closed source |
Cache-hit pricing applies when you re-send the same prefix; MiMo caches it server-side and bills the cached portion at the cache rate. GPT-4o mini has no equivalent tier — all input is billed at $0.15/M regardless. See the Token Cost Calculator for a live cost comparison with a cache-hit slider.
2. Side-by-side cost on a 1M-token workload
Pick a realistic workload: 1M input tokens, 2k output tokens, 1 request, 50% cache-hit ratio.
| Model | Input cost | Output cost | Total | vs MiMo |
|---|---|---|---|---|
| MiMo-V2.5 standard (50% cache) | 500k × $0.0028 + 500k × $0.14 = $0.0714 | 2k × $0.28 = $0.0006 | $0.072 | baseline |
| MiMo-V2.5 standard (0% cache, list) | 1M × $0.14 = $0.14 | 2k × $0.28 = $0.0006 | $0.141 | 2.0× |
| MiMo-V2.5-Pro (50% cache) | 500k × $0.0035 + 500k × $0.42 = $0.212 | 2k × $0.83 = $0.0017 | $0.214 | 3.0× |
| GPT-4o mini (no cache) | 1M × $0.15 = $0.15 | 2k × $0.60 = $0.0012 | $0.151 | 2.1× |
At 50% cache-hit (typical for chat / RAG / agent), MiMo-V2.5 standard is 2.1× cheaper than GPT-4o mini on the same workload. At 90% cache-hit (typical for production agents with stable system prompts), MiMo-V2.5 standard cost drops to $0.018 — 8.4× cheaper than GPT-4o mini. MiMo-V2.5-Pro at 90% cache is $0.025 — 6× cheaper than GPT-4o mini and 33× cheaper than GPT-4o (full).
3. Capability comparison
| Dimension | MiMo-V2.5 standard | MiMo-V2.5-Pro | GPT-4o mini |
|---|---|---|---|
| Context window | 1M | 1M | 128k |
| License | MIT | MIT | Closed (OpenAI) |
| Self-hostable | Yes (309B MoE, 80GB+ VRAM) | Yes (1T+ MoE, 320GB+ VRAM cluster) | No |
| OpenAI-compatible API | Yes | Yes | Yes (native) |
| Vision input | Limited (text primary; Omni variant for vision) | Limited (text primary; Omni variant for vision) | Yes (image + audio) |
| Tool / function calling | Yes | Yes | Yes |
| Reasoning mode ("thinking") | Yes (extended thinking via API parameter) | Yes (extended thinking via API parameter) | Yes (o1-style, separate models) |
| Multimodal model in family | Yes — V2.5-Omni (text + image + video + audio) | Yes — V2.5-Omni (text + image + video + audio) | Yes — GPT-4o mini handles vision natively |
| Knowledge cutoff | 2025-Q4 (per April 2026 release) | 2025-Q4 (per April 2026 release) | 2023-Q4 (per OpenAI docs) |
| Cache hit support | Yes (98% off) | Yes (99% off) | No (no equivalent tier) |
| Batch API discount | Not yet (Token Plan discount instead) | Not yet | Yes (50% discount, async) |
| Reasoning depth (SWE-Bench Verified, internal MiMo benchmark) | Comparable to GPT-4o mini (within 2-3 pts) | Comparable to GPT-4o (within 3-5 pts) | Baseline (no public MiMo-side number) |
| CN-side compliance | Filed 2026-07-08 (Beijing-XiaomiPengPaiAI-202509080119) | Same filing as standard | Via Azure CN / partner (Baidu / Qwen on Apple Intelligence) |
4. Where GPT-4o mini still wins
Honest take — there are real reasons to pick GPT-4o mini over MiMo:
- Stronger vision in the base model. GPT-4o mini handles image input natively, in the same model call. MiMo-V2.5 standard / Pro are text-primary; you need MiMo-V2.5-Omni for vision, and that's a different model.
- OpenAI-native integrations. If you're building on the OpenAI platform (Assistants API, OpenAI function-calling format, OpenAI tools registry, OpenAI's batch API), the integration is straightforward. MiMo is OpenAI-API-compatible but you may need to tweak some endpoints.
- Azure OpenAI Service. If your enterprise is on Azure, GPT-4o mini is the default cheap model. MiMo's primary public API is on Xiaomi's platform; multi-region cloud presence is limited.
- Production SLA + reliability. GPT-4o mini has 99.9% uptime SLA via OpenAI Enterprise. MiMo's public API doesn't yet publish an SLA, though self-hosting puts you in full control of reliability.
- Stronger safety filtering defaults. OpenAI's content moderation layer is built into the API. MiMo ships without a built-in moderation layer — you wire your own if needed.
5. Where MiMo wins
- Cache-hit pricing. The single biggest cost difference for repeated-prefix workloads. 50-120× cheaper on the cached portion.
- Context window. 1M vs 128k. If your workflow involves anything >128k (code review, long documents, agent memory), GPT-4o mini is out.
- Self-hosting. MIT weights mean you can run MiMo-7B at INT4 on consumer hardware (8GB RAM). GPT-4o mini cannot be self-hosted.
- Data sovereignty. MiMo-7B + V2.5 standard can be deployed fully on-device in your datacenter, with no data leaving the perimeter.
- Cost at scale. For 100M+ tokens/month, MiMo-V2.5 standard + cache-hit is the cheapest general-purpose model on the market.
- Chinese language quality. MiMo is trained by Xiaomi and significantly outperforms Western models on Chinese-language benchmarks. If you have a CN-side user base, this is meaningful.
6. Migration tips from GPT-4o mini to MiMo
Because MiMo is OpenAI-API-compatible, the migration is mostly a base-URL change.
pip install openai
from openai import OpenAI
# Old: GPT-4o mini
# client = OpenAI(api_key="sk-...")
# New: MiMo-V2.5 standard (drop-in)
client = OpenAI(
api_key="<your-miMo-api-key>",
base_url="https://api.xiaomimimo.com/v1",
)
response = client.chat.completions.create(
model="mimo-v2.5", # was: gpt-4o-mini
messages=[{"role": "user", "content": "Hello"}],
)
# Response shape is identical (id, choices, usage) — no client code changes
Three caveats:
- Image inputs: GPT-4o mini accepts image data inline; MiMo-V2.5 standard does not. Switch to MiMo-V2.5-Omni (model name
mimo-v2.5-omni) for vision. - Tool calling format: Both support OpenAI's
toolsarray, but MiMo's instruction-following on multi-step tool use is somewhat less robust. If your agent uses 3+ tool calls per turn, do a regression test before fully migrating. - Function name conventions: GPT-4o mini's training is biased toward OpenAI-canonical function names. You may need to give the model more explicit descriptions in
function.descriptionfor MiMo.
For the SDK migration with cache-hit enabled, you typically need to add a cached_content block or use a framework like mimo-claude-code (drop-in Claude Code CLI replacement) that auto-handles the caching.
7. When to use each
Decision tree:
- Chat / agent / RAG with repeated prefixes → MiMo-V2.5 standard. Cache-hit pricing is unbeatable.
- Code review or anything > 128k tokens → MiMo-V2.5-Pro. GPT-4o mini can't fit.
- Production vision app (image in, text out) → GPT-4o mini (or MiMo-V2.5-Omni if you're OK with a model swap).
- On-device / air-gapped / data-sovereign → MiMo-7B (self-host, free).
- OpenAI Assistants / Azure-only workflow → GPT-4o mini (stick with the platform you're on).
- CN-market app → MiMo (compliance filed 7-8, language quality better).
Related pages
- MiMo vs Claude Haiku 3.5 — same family of comparison against Anthropic's cheap model
- MiMo vs DeepSeek V4 Pro — both are open-weights reasoning models; positioning differs
- Token Cost Calculator — live cost comparison with cache-hit slider
- Pricing hub — full V2.5 lineup with cache-hit and list columns
- MiMo-V2.5 Series deep dive — what V2.5 standard and V2.5-Pro can actually do
Pricing verified against the official 2026-05-27 MiMo announcement and OpenAI's public pricing page on July 20, 2026. Last updated 2026-07-20.