API cost decision in 10 seconds

Gemma 4 31B vs Kimi K2 0711

Pick Gemma 4 31B when budget and context both matter.

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

Budget verdict

Pick Gemma 4 31B when budget and context both matter.

On the standard 1M input plus 500K output workload, Gemma 4 31B is estimated at $0.3 vs $1.72 for Kimi K2 0711, saving $1.41 (82.3% lower).

Cost-first pickGemma 4 31B
Context-first pickGemma 4 31B
Sample savings$1.4182.3%
10x traffic gap$14.15

Gemma 4 31B is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $14.15. Use the calculator below to replace the sample workload with your own token volume.

Cost sensitivity

Workload Sensitivity

Same prices, different token mixes.

Gemma 4 31B stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickGemma 4 31BKimi K2 0711
Input-heavy / RAG5M input + 500K outputGemma 4 31B$0.78$4
Balanced workload1M input + 1M outputGemma 4 31B$0.49$2.87
Output-heavy chatbot1M input + 5M outputGemma 4 31B$1.97$12.07
Cheaper input Gemma 4 31B $0.12 vs $0.57 / 1M

Gemma 4 31B is $0.45 cheaper per 1M input tokens (78.9% lower; 4.75x difference).

Cheaper output Gemma 4 31B $0.37 vs $2.3 / 1M

Gemma 4 31B is $1.93 cheaper per 1M output tokens (83.9% lower; 6.22x difference).

Larger context Gemma 4 31B 262.14K vs 131.07K

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

Sample workload Gemma 4 31B $0.3 vs $1.72

Gemma 4 31B is $1.41 cheaper on the standard workload (82.3% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Gemma 4 31B Calculating… Estimated API cost
Kimi K2 0711 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

Gemma 4 31B has the lower input price; Gemma 4 31B has the lower output price; Gemma 4 31B offers the larger context window. For the 1M input plus 500K output sample, Gemma 4 31B 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 $1.72 for Kimi K2 0711.

Best Fit

Choose Gemma 4 31B when you care most about lower input-token price, lower output-token price, and larger context window.

Choose Kimi K2 0711 when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

Decision Notes
  • On the standard 1M input plus 500K output workload, Gemma 4 31B is estimated at $0.3 vs $1.72 for Kimi K2 0711, saving $1.41 (82.3% lower).
  • Gemma 4 31B is $1.41 cheaper on the standard workload (82.3% lower).
  • Gemma 4 31B is $0.45 cheaper per 1M input tokens (78.9% lower; 4.75x difference).
  • Gemma 4 31B is $1.93 cheaper per 1M output tokens (83.9% lower; 6.22x difference).
  • Gemma 4 31B has 131.07K more context (2x larger).
Head-to-Head Specs
FeatureGemma 4 31B
(Google)
Kimi K2 0711
(MoonshotAI)
Input Price
prompt tokens per 1M
$0.12$0.57
Completion Price
per 1M tokens
$0.37$2.3
Sample Workload Cost
1M input + 500K output
$0.3$1.72
Context Window262.14K131.07K
Release Date
Popularity#27#140

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionGemma 4 31BOn the standard 1M input plus 500K output workload, Gemma 4 31B is estimated at $0.3 vs $1.72 for Kimi K2 0711, saving $1.41 (82.3% lower).
High-volume input processingGemma 4 31BLower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsGemma 4 31BLower 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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MoonshotAI 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...

Kimi K2 0711

Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It is optimized for...