API cost decision in 10 seconds

MiMo-V2.5 vs Qwen3.5-122B-A10B

Pick Qwen3.5-122B-A10B for lower cost; pick MiMo-V2.5 only if the larger context window matters more.

Page updated:  Data confirmed:  Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Qwen3.5-122B-A10B for lower cost; pick MiMo-V2.5 only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Qwen3.5-122B-A10B is estimated at $1.3 vs $1.4 for MiMo-V2.5, saving $0.1 (7.1% lower).

Cost-first pickQwen3.5-122B-A10B
Context-first pickMiMo-V2.5
Sample savings$0.17.1%
10x traffic gap$1

MiMo-V2.5 has more context, but Qwen3.5-122B-A10B saves $0.1 on the standard workload. At 10x that traffic, the same price gap is about $1. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Cost winner changes by workload shape: input-heavy / RAG favors Qwen3.5-122B-A10B, balanced workload favors Qwen3.5-122B-A10B, and output-heavy chatbot favors MiMo-V2.5.

Workload shapeToken mixBetter pickMiMo-V2.5Qwen3.5-122B-A10B
Input-heavy / RAG5M input + 500K outputQwen3.5-122B-A10B$3$2.34
Balanced workload1M input + 1M outputQwen3.5-122B-A10B$2.4$2.34
Output-heavy chatbot1M input + 5M outputMiMo-V2.5$10.4$10.66
Cheaper input Qwen3.5-122B-A10B $0.4 vs $0.26 / 1M

Qwen3.5-122B-A10B is $0.14 cheaper per 1M input tokens (35% lower; 1.54x difference).

Cheaper output MiMo-V2.5 $2 vs $2.08 / 1M

MiMo-V2.5 is $0.08 cheaper per 1M output tokens (3.8% lower; 1.04x difference).

Larger context MiMo-V2.5 1.05M vs 262.14K

MiMo-V2.5 has 786.43K more context (4x larger).

Sample workload Qwen3.5-122B-A10B $1.4 vs $1.3

Qwen3.5-122B-A10B is $0.1 cheaper on the standard workload (7.1% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
MiMo-V2.5 Calculating… Estimated API cost
Qwen3.5-122B-A10B 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

Qwen3.5-122B-A10B has the lower input price; MiMo-V2.5 has the lower output price; MiMo-V2.5 offers the larger context window. For the 1M input plus 500K output sample, Qwen3.5-122B-A10B 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-V2.5 and $1.3 for Qwen3.5-122B-A10B.

Best Fit

Choose MiMo-V2.5 when you care most about lower output-token price, and larger context window.

Choose Qwen3.5-122B-A10B when you care most about lower input-token price.

Decision Notes
  • On the standard 1M input plus 500K output workload, Qwen3.5-122B-A10B is estimated at $1.3 vs $1.4 for MiMo-V2.5, saving $0.1 (7.1% lower).
  • Qwen3.5-122B-A10B is $0.1 cheaper on the standard workload (7.1% lower).
  • Qwen3.5-122B-A10B is $0.14 cheaper per 1M input tokens (35% lower; 1.54x difference).
  • MiMo-V2.5 is $0.08 cheaper per 1M output tokens (3.8% lower; 1.04x difference).
  • MiMo-V2.5 has 786.43K more context (4x larger).
Head-to-Head Specs
FeatureMiMo-V2.5
(Xiaomi)
Qwen3.5-122B-A10B
(Qwen)
Input Price
prompt tokens per 1M
$0.4$0.26
Completion Price
per 1M tokens
$2$2.08
Sample Workload Cost
1M input + 500K output
$1.4$1.3
Context Window1.05M262.14K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionQwen3.5-122B-A10BOn the standard 1M input plus 500K output workload, Qwen3.5-122B-A10B is estimated at $1.3 vs $1.4 for MiMo-V2.5, saving $0.1 (7.1% lower).
High-volume input processingQwen3.5-122B-A10BLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsMiMo-V2.5Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workMiMo-V2.5A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Larger context near this budget
  • Llama 4 Scout offers 10M context with $0.23 sample workload cost.
  • Owl Alpha offers 1.05M context with $0 sample workload cost.

Cheaper alternatives

Review low-cost models sorted by a standard 1M input plus 500K output workload.

Open cheapest models

Larger context alternatives

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

Open largest context models

Provider catalogs

Compare models within provider hubs before choosing a final API vendor.

Open provider hubs

Xiaomi catalog

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

Open Xiaomi models

Qwen catalog

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

Open Qwen models
MiMo-V2.5

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...

Qwen3.5-122B-A10B

The Qwen3.5 122B-A10B 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. In terms of...