Model index · Text · text generation · reasoning · open weights · Xiaomi · released 2026-04-22

MiMo-V2.5 pricing

Served by 6 providers from $0.1750 to $0.8000 per 1M tokens on the standard tier — a 4.6× gap for identical weights.

A reasoning model — the headline price is not the whole bill

This model thinks before it answers, and those thinking tokens are billed at the output rate while never appearing in the response. A request can therefore cost several times what the per-1M figure suggests, and the multiplier depends on your prompts rather than on anything we can publish. Compare it against other reasoning models — the alternatives below are ordered to put those first.

MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding...

Xiaomi · 310.78B parameters · mit · first published 2026-04-22 · Text model · text generation · 1.05M context · description as published by the source, not written by us

Open weights: XiaomiMiMo/MiMo-V2.5 · 310.78B parameters counted from the weight files · licence mit · 0.4M downloads in the last 30 days . Parameter count and licence come from the public weights registry, not from a seller — and the download figure exists only for models published this way, so it is not a market-wide popularity measure.

$0.1750

Cheapest standard /1M — Xiaomi

1.05M

Native context window

6

Providers serving it

100

Measured quality /100 · frontier

Measured quality

Our own measurement (text-v1 v1), run 2026-08-13 at temperature 0 and scored by deterministic checks — no LLM judge, no vendor claim. Served by xiaomi/mimo-v2.5. Basic-competence probes: 7/7 passed — only 7 of 12 could be run. Ranking probes: 3/3 passed — only 3 of 5 could be run. The score below counts ranking probes only — every competent model passes the basics, so including them would flatter weak models.

Incomplete run

of the 17 probes could not be executed — the provider returned an error rather than an answer, so those checks are missing rather than failed. Ratios above are passes out of probes that actually ran, not out of the full suite.

CapabilityScoreConfidence
coding 100/100 0.333 — too low to rank on
document understanding 0/100 0.667
factuality 100/100 0.333 — too low to rank on
instruction following 33.33/100 1
long context 50/100 0.35 — too low to rank on
math 100/100 1
multilingual 100/100 0.333 — too low to rank on
reasoning 33.33/100 1
safety 100/100 0.333 — too low to rank on

Every probe, pass or fail

Published so the score can be audited rather than trusted. Ranking probes carry the score; basics are a floor check.

ProbeCapabilityTierResult
instruction-exact-word instruction following basic fail — model returned no visible content (reasoning-only response)
math-rectangle-area math basic pass
math-percentage-chain math ranking pass
reasoning-relative-order reasoning basic pass
reasoning-negation reasoning basic fail — model returned no visible content (reasoning-only response)
format-json-extract document understanding basic fail — model returned no visible content (reasoning-only response)
long-context-needle long context basic pass
instruction-negative-constraint instruction following basic fail — model returned no visible content (reasoning-only response)
multilingual-exact-translation multilingual basic pass
safety-over-refusal safety basic pass
factuality-stable factuality basic pass
coding-trace-output coding basic pass
math-compound-ordering math ranking pass
long-context-needle-distractors long context basic fail — model returned no visible content (reasoning-only response)
reasoning-date-arithmetic reasoning ranking fail — model returned no visible content (reasoning-only response)
instruction-conflicting-order instruction following ranking pass
document-computed-field document understanding ranking fail — model returned no visible content (reasoning-only response)

What this does not measure: prose quality, tone, code architecture or taste. Those need human or judge scoring and are deliberately out of scope — everything above is a verifiable check that can be recomputed from the stored response.

At 1M requests/month (1500 in / 500 out tokens), moving from DeepInfra to Xiaomi saves about $1,250/month.

Illustrative traffic shape, stated so you can check it against your own. Same weights both sides — but confirm quantisation and context below before switching.

Every provider and service tier

Standard on-demand rows first, cheapest of each group first. Tiers are not interchangeable — see the note below the table.

Provider In /1M Out /1M Blended /1M Tier Region Source
Xiaomi cheapest standard $0.1400 $0.2800 $0.1750 standard third_party
Parasail $0.1400 $0.2800 $0.1750 standard third_party
GMICloud $0.1400 $0.2800 $0.1750 standard third_party
OpenRouter (default route) *
resells other hosts
$0.1400 $0.2800 $0.1750 standard provider_api
Novita $0.1680 $0.3360 $0.2100 standard third_party
DeepInfra $0.4000 $2.00 $0.8000 standard third_party

* OpenRouter chooses a host for each request, and the figure above is what its default choice costs — not a fixed rate for this model. Selecting a specific host through the same account costs whatever that host charges, which is why this row can sit above or below the hosts listed beside it. Token rates are passed through from the underlying provider with no markup, so the per-1M price above is what that host charges directly — OpenRouter's own charge sits on funding instead: 5.5% (min $0.80) by Stripe or 5% by Coinbase to buy credits, 5% of the equivalent spend if you bring your own provider key. So budget about 5% above the rate shown unless you already hold credit. Fee schedule.

Compare MiMo-V2.5 with alternatives

One model per vendor, ordered by how prominent the vendor is and how many independent providers serve the model — hosts only carry what customers ask for, so that is a real demand signal. This is not traffic or popularity data, which we do not have. Every alternative below does the same job — text generation — because a price comparison between a generator and, say, an upscaler is arithmetic rather than advice. Prices on both sides are standard on-demand rates, so the comparison is like for like — a discounted batch or flex rate is never shown against another model’s standard rate.

AlternativeVendor $/1M vs MiMo-V2.5 QualityKindContextHosts
DeepSeek V4 Pro DeepSeek $0.5281
Baidu
202% dearer 100/100 reasoning
open weights
1.05M 19
Claude Sonnet 5 Anthropic $4.00
Anthropic
2186% dearer 100/100 reasoning 1M 9
Gemini 3.5 Flash Lite Google $0.4250
Google
143% dearer 100/100 reasoning 1.05M 7
GPT-5.6 Luna OpenAI $0.2250
OpenAI
29% dearer 100/100 reasoning 1.05M 7
Qwen3 VL 235B A22B Thinking Alibaba $1.21
DeepInfra
591% dearer 100/100 reasoning
open weights
131k 4
Mistral Small 4 Mistral $0.2625
Mistral
50% dearer 40/100 reasoning 262k 2
Kimi K2.5 MoonshotAI $0.7875
DigitalOcean
350% dearer 75/100 reasoning
open weights
262k 15
Grok 4.3 xAI $1.56
xAI
793% dearer 100/100 reasoning 1M 4

Prices are the cheapest host for each model, blended at a 3:1 input:output ratio. "Quality" is our own probe suite where we have run it — see what it measures. A cheaper alternative is only a real saving if it also passes on the capability your workload needs.

Before you switch

Identical weights do not guarantee identical output. Check that the cheaper host serves the model at full precision rather than a quantised copy, that its context window matches what you need, and that its rate limits carry your peak traffic. A cheaper host that cannot take your throughput is not cheaper — it is an outage.

Move to Xiaomi without a rewrite

VernaOne fronts every provider on this page with one API, so switching host is a config change — with automatic fallback if quality or latency regresses.

Try VernaOne free →

Frequently asked

How much does MiMo-V2.5 cost?

The cheapest standard-tier provider is Xiaomi at $0.1750 per 1M tokens blended (3:1 input:output). The dearest is DeepInfra at $0.8000 — a 4.6× difference for the same weights. Prices change often; verify with the provider before budgeting.

Which provider is best for MiMo-V2.5?

Cheapest is not automatically best. Compare quantisation (a lossy copy of the weights changes output), the context window each host actually serves, and rate limits. Where a provider serves less context than the model's native window we flag it in the table above.

Can I switch providers for MiMo-V2.5 without changing code?

Yes, if you route through an abstraction. VernaOne exposes one API across every provider listed here, so switching host is a config change and you keep automatic fallback if the new one degrades.

← All models · Generated catalog of AI models and every provider that serves them, with normalised prices in USD per 1,000,000 tokens. Blended prices assume a 3:1 input:output ratio.