Mimo-V2-Flash
qiniu-ai/mimo-v2-flashمن qiniu-ai · العائلة: mimo · أُصدِر 2025-12-16 · تاريخ المعرفة: 2024-12-01
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
القدرات
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding59
- Tool calling40/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)8/20
- Provider availability1/10
Agents66
- Tool calling35/35
- Structured output0/25
- Reasoning15/15
- Output token limit15/15
- Provider availability1/10
JSON / structured output49
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume19/20
Cost efficiency77
- Headline price (log-scaled)72/95
- Has prompt-cache pricing5/5
Long context60
- Context window (100K → 2M)50/90
- Has published price for full window10/10
Production-readiness65
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.65 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $1.30 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.35 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $1.10 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $1.38 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
تفاصيل التسعير
السعر المُوصى به من qiniu-ai · mimo-v2-flash
متاح لدى 1 مزود
| المزود | معرف نموذج المزود | إدخال / 1M | إخراج / 1M | السياق | تاريخ الإصدار |
|---|---|---|---|---|---|
| Qiniu qiniu-ai | mimo-v2-flash | $0.100 | $0.300 | 256K | 2025-12-16 |
Frequently asked questions
How much does Mimo-V2-Flash cost?
Mimo-V2-Flash costs $0.100 per 1M input tokens and $0.300 per 1M output tokens, sourced from qiniu-ai. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Mimo-V2-Flash?
Mimo-V2-Flash has a context window of 256K tokens, with a max output of 256K tokens per reply. This is the total combined size of prompt + completion.
Does Mimo-V2-Flash support tool calling?
Yes. Mimo-V2-Flash supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Mimo-V2-Flash support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Mimo-V2-Flash in our data source. Verify with qiniu-ai's official documentation before relying on it in production.
Can Mimo-V2-Flash accept image input?
No. Mimo-V2-Flash only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Mimo-V2-Flash open-weight?
Yes. Mimo-V2-Flash's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Mimo-V2-Flash?
If Mimo-V2-Flash doesn't fit, consider Kling-V2 6. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More qiniu-ai models
- Kling-V2 6Unknown pricing
Capability lists this model is in
آخر تحديث:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.