API cost decision in 10 seconds

Gemma 4 31B vs Ministral 3 3B 2512

Pick Ministral 3 3B 2512 for lower cost; pick Gemma 4 31B only if the larger context window matters more.

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

Budget verdict

Pick Ministral 3 3B 2512 for lower cost; pick Gemma 4 31B only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Ministral 3 3B 2512 is estimated at $0.15 vs $0.3 for Gemma 4 31B, saving $0.15 (50.8% lower).

Cost-first pickMinistral 3 3B 2512
Context-first pickGemma 4 31B
Sample savings$0.1550.8%
10x traffic gap$1.55

Gemma 4 31B has more context, but Ministral 3 3B 2512 saves $0.15 on the standard workload. At 10x that traffic, the same price gap is about $1.55. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Ministral 3 3B 2512 stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickGemma 4 31BMinistral 3 3B 2512
Input-heavy / RAG5M input + 500K outputMinistral 3 3B 2512$0.78$0.55
Balanced workload1M input + 1M outputMinistral 3 3B 2512$0.49$0.2
Output-heavy chatbot1M input + 5M outputMinistral 3 3B 2512$1.97$0.6
Cheaper input Ministral 3 3B 2512 $0.12 vs $0.1 / 1M

Ministral 3 3B 2512 is $0.02 cheaper per 1M input tokens (16.7% lower; 1.2x difference).

Cheaper output Ministral 3 3B 2512 $0.37 vs $0.1 / 1M

Ministral 3 3B 2512 is $0.27 cheaper per 1M output tokens (73% lower; 3.7x difference).

Larger context Gemma 4 31B 262.14K vs 131.07K

Gemma 4 31B has 131.07K more context (2x larger).

Sample workload Ministral 3 3B 2512 $0.3 vs $0.15

Ministral 3 3B 2512 is $0.15 cheaper on the standard workload (50.8% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Gemma 4 31B Calculating… Estimated API cost
Ministral 3 3B 2512 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

Ministral 3 3B 2512 has the lower input price; Ministral 3 3B 2512 has the lower output price; Gemma 4 31B offers the larger context window. For the 1M input plus 500K output sample, Ministral 3 3B 2512 is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.3 for Gemma 4 31B and $0.15 for Ministral 3 3B 2512.

Best Fit

Choose Gemma 4 31B when you care most about larger context window.

Choose Ministral 3 3B 2512 when you care most about lower input-token price, and lower output-token price.

Decision Notes
  • On the standard 1M input plus 500K output workload, Ministral 3 3B 2512 is estimated at $0.15 vs $0.3 for Gemma 4 31B, saving $0.15 (50.8% lower).
  • Ministral 3 3B 2512 is $0.15 cheaper on the standard workload (50.8% lower).
  • Ministral 3 3B 2512 is $0.02 cheaper per 1M input tokens (16.7% lower; 1.2x difference).
  • Ministral 3 3B 2512 is $0.27 cheaper per 1M output tokens (73% lower; 3.7x difference).
  • Gemma 4 31B has 131.07K more context (2x larger).
Head-to-Head Specs
FeatureGemma 4 31B
(Google)
Ministral 3 3B 2512
(Mistral)
Input Price
prompt tokens per 1M
$0.12$0.1
Completion Price
per 1M tokens
$0.37$0.1
Sample Workload Cost
1M input + 500K output
$0.3$0.15
Context Window262.14K131.07K
Release Date

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionMinistral 3 3B 2512On the standard 1M input plus 500K output workload, Ministral 3 3B 2512 is estimated at $0.15 vs $0.3 for Gemma 4 31B, saving $0.15 (50.8% lower).
High-volume input processingMinistral 3 3B 2512Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsMinistral 3 3B 2512Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workGemma 4 31BA larger context window leaves more room for retrieved passages, conversation history, or source files.

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Google catalog

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Mistral catalog

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Gemma 4 31B

Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...

Ministral 3 3B 2512

The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.