API cost decision in 10 seconds

MiMo-V2-Omni vs Qwen3.5 397B A17B

Pick MiMo-V2-Omni when budget is the priority.

Pricing data updated:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick MiMo-V2-Omni when budget is the priority.

On the standard 1M input plus 500K output workload, MiMo-V2-Omni is estimated at $1.4 vs $1.56 for Qwen3.5 397B A17B, saving $0.16 (10.3% lower).

Cost-first pickMiMo-V2-Omni
Context-first pickBoth models
Sample savings$0.1610.3%
10x traffic gap$1.6

The reported context window is tied, so cost and provider fit carry more weight. At 10x that traffic, the same price gap is about $1.6. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

MiMo-V2-Omni stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickMiMo-V2-OmniQwen3.5 397B A17B
Input-heavy / RAG5M input + 500K outputMiMo-V2-Omni$3$3.12
Balanced workload1M input + 1M outputMiMo-V2-Omni$2.4$2.73
Output-heavy chatbot1M input + 5M outputMiMo-V2-Omni$10.4$12.09
Cheaper input Qwen3.5 397B A17B $0.4 vs $0.39 / 1M

Qwen3.5 397B A17B is $0.01 cheaper per 1M input tokens (2.5% lower; 1.03x difference).

Cheaper output MiMo-V2-Omni $2 vs $2.34 / 1M

MiMo-V2-Omni is $0.34 cheaper per 1M output tokens (14.5% lower; 1.17x difference).

Larger context Tie 262.14K vs 262.14K

Both models report the same context window at 262.14K tokens.

Sample workload MiMo-V2-Omni $1.4 vs $1.56

MiMo-V2-Omni is $0.16 cheaper on the standard workload (10.3% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
MiMo-V2-Omni Calculating… Estimated API cost
Qwen3.5 397B A17B Calculating… Estimated API cost
Cheaper for this workload Calculating… Difference: calculating…

This estimate uses normalized public API pricing per 1M tokens. It is a planning aid, not a billing quote. Verify provider pricing, limits, and terms before production use.

Quick Decision

Verdict

Qwen3.5 397B A17B has the lower input price; MiMo-V2-Omni has the lower output price; both models report the same context window. For the 1M input plus 500K output sample, MiMo-V2-Omni is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $1.4 for MiMo-V2-Omni and $1.56 for Qwen3.5 397B A17B.

Best Fit

Choose MiMo-V2-Omni when you care most about lower output-token price.

Choose Qwen3.5 397B A17B when you care most about lower input-token price.

Decision Notes
  • On the standard 1M input plus 500K output workload, MiMo-V2-Omni is estimated at $1.4 vs $1.56 for Qwen3.5 397B A17B, saving $0.16 (10.3% lower).
  • MiMo-V2-Omni is $0.16 cheaper on the standard workload (10.3% lower).
  • Qwen3.5 397B A17B is $0.01 cheaper per 1M input tokens (2.5% lower; 1.03x difference).
  • MiMo-V2-Omni is $0.34 cheaper per 1M output tokens (14.5% lower; 1.17x difference).
  • Both models report the same context window at 262.14K tokens.
Head-to-Head Specs
FeatureMiMo-V2-Omni
(Xiaomi)
Qwen3.5 397B A17B
(Qwen)
Input Price
prompt tokens per 1M
$0.4$0.39
Completion Price
per 1M tokens
$2$2.34
Sample Workload Cost
1M input + 500K output
$1.4$1.56
Context Window262.14K262.14K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionMiMo-V2-OmniOn the standard 1M input plus 500K output workload, MiMo-V2-Omni is estimated at $1.4 vs $1.56 for Qwen3.5 397B A17B, saving $0.16 (10.3% lower).
High-volume input processingQwen3.5 397B A17BLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsMiMo-V2-OmniLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workTieA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

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Popular competitors
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Cheaper alternatives

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Larger context alternatives

Find models with larger context windows for RAG, long documents, and codebase review.

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Provider catalogs

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Xiaomi catalog

Review all tracked Xiaomi models before deciding whether this matchup is the right shortlist.

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Qwen catalog

Check other Qwen models with comparable pricing, context, or release timing.

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MiMo-V2-Omni

MiMo-V2-Omni is a frontier omni-modal model that natively processes image, video, and audio inputs within a unified architecture. It combines strong multimodal perception with agentic capability - visual grounding, multi-step...

Qwen3.5 397B A17B

The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers...