API cost decision in 10 seconds

🔥MiMo-V2.5-Pro vs DeepSeek V3.1 Terminus

Pick DeepSeek V3.1 Terminus for lower cost; pick MiMo-V2.5-Pro 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 DeepSeek V3.1 Terminus for lower cost; pick MiMo-V2.5-Pro only if the larger context window matters more.

On the standard 1M input plus 500K output workload, DeepSeek V3.1 Terminus is estimated at $0.74 vs $0.87 for MiMo-V2.5-Pro, saving $0.12 (14.4% lower).

Cost-first pickDeepSeek V3.1 Terminus
Context-first pickMiMo-V2.5-Pro
Sample savings$0.1214.4%
10x traffic gap$1.25

MiMo-V2.5-Pro has more context, but DeepSeek V3.1 Terminus saves $0.12 on the standard workload. At 10x that traffic, the same price gap is about $1.25. 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 DeepSeek V3.1 Terminus, balanced workload favors DeepSeek V3.1 Terminus, and output-heavy chatbot favors MiMo-V2.5-Pro.

Workload shapeToken mixBetter pickMiMo-V2.5-ProDeepSeek V3.1 Terminus
Input-heavy / RAG5M input + 500K outputDeepSeek V3.1 Terminus$2.61$1.83
Balanced workload1M input + 1M outputDeepSeek V3.1 Terminus$1.3$1.22
Output-heavy chatbot1M input + 5M outputMiMo-V2.5-Pro$4.78$5.02
Cheaper input DeepSeek V3.1 Terminus $0.435 vs $0.27 / 1M

DeepSeek V3.1 Terminus is $0.16 cheaper per 1M input tokens (37.9% lower; 1.61x difference).

Cheaper output MiMo-V2.5-Pro $0.87 vs $0.95 / 1M

MiMo-V2.5-Pro is $0.08 cheaper per 1M output tokens (8.4% lower; 1.09x difference).

Larger context MiMo-V2.5-Pro 1.05M vs 163.84K

MiMo-V2.5-Pro has 884.74K more context (6.4x larger).

Sample workload DeepSeek V3.1 Terminus $0.87 vs $0.74

DeepSeek V3.1 Terminus is $0.12 cheaper on the standard workload (14.4% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
MiMo-V2.5-Pro Calculating… Estimated API cost
DeepSeek V3.1 Terminus 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

DeepSeek V3.1 Terminus has the lower input price; MiMo-V2.5-Pro has the lower output price; MiMo-V2.5-Pro offers the larger context window. For the 1M input plus 500K output sample, DeepSeek V3.1 Terminus is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.87 for MiMo-V2.5-Pro and $0.74 for DeepSeek V3.1 Terminus.

Best Fit

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

Choose DeepSeek V3.1 Terminus when you care most about lower input-token price.

Decision Notes
  • On the standard 1M input plus 500K output workload, DeepSeek V3.1 Terminus is estimated at $0.74 vs $0.87 for MiMo-V2.5-Pro, saving $0.12 (14.4% lower).
  • DeepSeek V3.1 Terminus is $0.12 cheaper on the standard workload (14.4% lower).
  • DeepSeek V3.1 Terminus is $0.16 cheaper per 1M input tokens (37.9% lower; 1.61x difference).
  • MiMo-V2.5-Pro is $0.08 cheaper per 1M output tokens (8.4% lower; 1.09x difference).
  • MiMo-V2.5-Pro has 884.74K more context (6.4x larger).
Head-to-Head Specs
Feature🔥MiMo-V2.5-Pro
(Xiaomi)
DeepSeek V3.1 Terminus
(DeepSeek)
Input Price
prompt tokens per 1M
$0.435$0.27
Completion Price
per 1M tokens
$0.87$0.95
Sample Workload Cost
1M input + 500K output
$0.87$0.74
Context Window1.05M163.84K
Release Date
Popularity#13#91

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionDeepSeek V3.1 TerminusOn the standard 1M input plus 500K output workload, DeepSeek V3.1 Terminus is estimated at $0.74 vs $0.87 for MiMo-V2.5-Pro, saving $0.12 (14.4% lower).
High-volume input processingDeepSeek V3.1 TerminusLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsMiMo-V2.5-ProLower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workMiMo-V2.5-ProA larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps
  • MiMo-V2-Flash can replace MiMo-V2.5-Pro when lower sample workload cost matters most: $0.25.
  • MiMo-V2.5 can replace MiMo-V2.5-Pro when lower sample workload cost matters most: $0.28.
  • DeepSeek V4 Flash (free) can replace DeepSeek V3.1 Terminus when lower sample workload cost matters most: $0.
  • DeepSeek V4 Flash can replace DeepSeek V3.1 Terminus when lower sample workload cost matters most: $0.2.
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.

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Xiaomi catalog

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

Open Xiaomi models

DeepSeek catalog

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

Open DeepSeek models
MiMo-V2.5-Pro

MiMo-V2.5-Pro is Xiaomi’s flagship model, delivering strong performance in general agentic capabilities, complex software engineering, and long-horizon tasks, with top rankings on benchmarks such as ClawEval, GDPVal, and SWE-bench Pro....

DeepSeek V3.1 Terminus

DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's...