API cost decision in 10 seconds

Gemma 4 31B vs Mistral Nemo

Pick Mistral Nemo for lower cost; pick Gemma 4 31B only if the larger context window matters more.

Page updated: 2026-05-26T15:08:08Z Data confirmed: 2026-05-26T18:08:02Z Prices normalized to USD per 1M tokens Sample workload: 1M input + 500K output

Budget verdict

Pick Mistral Nemo for lower cost; pick Gemma 4 31B only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Mistral Nemo is estimated at $0.04 vs $0.3 for Gemma 4 31B, saving $0.27 (88.5% lower).

Cost-first pickMistral Nemo

Context-first pickGemma 4 31B

Sample savings$0.2788.5%

10x traffic gap$2.7

Gemma 4 31B has more context, but Mistral Nemo saves $0.27 on the standard workload. At 10x that traffic, the same price gap is about $2.7. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Mistral Nemo stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shape	Token mix	Better pick	Gemma 4 31B	Mistral Nemo
Input-heavy / RAG	5M input + 500K output	Mistral Nemo	$0.78	$0.12
Balanced workload	1M input + 1M output	Mistral Nemo	$0.49	$0.05
Output-heavy chatbot	1M input + 5M output	Mistral Nemo	$1.97	$0.17

Cheaper input Mistral Nemo $0.12 vs $0.02 / 1M

Mistral Nemo is $0.1 cheaper per 1M input tokens (83.3% lower; 6x difference).

Cheaper output Mistral Nemo $0.37 vs $0.03 / 1M

Mistral Nemo is $0.34 cheaper per 1M output tokens (91.9% lower; 12.3x difference).

Larger context Gemma 4 31B 262.14K vs 131.07K

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

Sample workload Mistral Nemo $0.3 vs $0.04

Mistral Nemo is $0.27 cheaper on the standard workload (88.5% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.

Monthly input tokens Monthly output tokens

Gemma 4 31B Calculating… Estimated API cost

Mistral Nemo 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

Mistral Nemo has the lower input price; Mistral Nemo has the lower output price; Gemma 4 31B offers the larger context window. For the 1M input plus 500K output sample, Mistral Nemo 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 $0.04 for Mistral Nemo.

Best Fit

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

Choose Mistral Nemo 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, Mistral Nemo is estimated at $0.04 vs $0.3 for Gemma 4 31B, saving $0.27 (88.5% lower).
Mistral Nemo is $0.27 cheaper on the standard workload (88.5% lower).
Mistral Nemo is $0.1 cheaper per 1M input tokens (83.3% lower; 6x difference).
Mistral Nemo is $0.34 cheaper per 1M output tokens (91.9% lower; 12.3x difference).
Gemma 4 31B has 131.07K more context (2x larger).

Head-to-Head Specs

Feature	Gemma 4 31B (Google)	Mistral Nemo (Mistral)
Input Price prompt tokens per 1M	$0.12	$0.02
Completion Price per 1M tokens	$0.37	$0.03
Sample Workload Cost 1M input + 500K output	$0.3	$0.04
Context Window	262.14K	131.07K
Release Date	2026-04-02	2024-07-19
Popularity	#27	#34

Use-Case Decision Matrix

Use case	Better pick	Why
Budget-constrained production	Mistral Nemo	On the standard 1M input plus 500K output workload, Mistral Nemo is estimated at $0.04 vs $0.3 for Gemma 4 31B, saving $0.27 (88.5% lower).
High-volume input processing	Mistral Nemo	Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbots	Mistral Nemo	Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document work	Gemma 4 31B	A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

Same-provider lower-cost swaps

Gemma 4 31B (free) can replace Gemma 4 31B when lower sample workload cost matters most: $0.
Gemma 4 26B A4B (free) can replace Gemma 4 31B when lower sample workload cost matters most: $0.
Lyria 3 Clip Preview can replace Gemma 4 31B when lower sample workload cost matters most: $0.
Lyria 3 Pro Preview can replace Gemma 4 31B when lower sample workload cost matters most: $0.

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.
DeepSeek V4 Flash offers 1.05M context with $0.2 sample workload cost.
Gemini 2.5 Flash Lite offers 1.05M context with $0.3 sample workload cost.

Popular competitors

DeepSeek V4 Flash · DeepSeek · #1
Hy3 preview · Tencent · #2
Claude Opus 4.7 · Anthropic · #3
Claude Sonnet 4.6 · Anthropic · #4

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

Google catalog

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

Open Google models

Mistral catalog

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

Open Mistral models

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...

Mistral Nemo

A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...