API cost decision in 10 seconds

Gemma 4 31B vs Qwen3 8B

Pick Qwen3 8B for lower cost; pick Gemma 4 31B 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 8B for lower cost; pick Gemma 4 31B only if the larger context window matters more.

On the standard 1M input plus 500K output workload, Qwen3 8B is estimated at $0.25 vs $0.3 for Gemma 4 31B, saving $0.05 (18% lower).

Cost-first pickQwen3 8B
Context-first pickGemma 4 31B
Sample savings$0.0518%
10x traffic gap$0.55

Gemma 4 31B has more context, but Qwen3 8B saves $0.05 on the standard workload. At 10x that traffic, the same price gap is about $0.55. 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 8B, balanced workload favors Qwen3 8B, and output-heavy chatbot favors Gemma 4 31B.

Workload shapeToken mixBetter pickGemma 4 31BQwen3 8B
Input-heavy / RAG5M input + 500K outputQwen3 8B$0.78$0.45
Balanced workload1M input + 1M outputQwen3 8B$0.49$0.45
Output-heavy chatbot1M input + 5M outputGemma 4 31B$1.97$2.05
Cheaper input Qwen3 8B $0.12 vs $0.05 / 1M

Qwen3 8B is $0.07 cheaper per 1M input tokens (58.3% lower; 2.4x difference).

Cheaper output Gemma 4 31B $0.37 vs $0.4 / 1M

Gemma 4 31B is $0.03 cheaper per 1M output tokens (7.5% lower; 1.08x difference).

Larger context Gemma 4 31B 262.14K vs 131.07K

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

Sample workload Qwen3 8B $0.3 vs $0.25

Qwen3 8B is $0.05 cheaper on the standard workload (18% lower).

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Gemma 4 31B Calculating… Estimated API cost
Qwen3 8B 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 8B 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, Qwen3 8B 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 $0.25 for Qwen3 8B.

Best Fit

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

Choose Qwen3 8B when you care most about lower input-token price.

Decision Notes
  • On the standard 1M input plus 500K output workload, Qwen3 8B is estimated at $0.25 vs $0.3 for Gemma 4 31B, saving $0.05 (18% lower).
  • Qwen3 8B is $0.05 cheaper on the standard workload (18% lower).
  • Qwen3 8B is $0.07 cheaper per 1M input tokens (58.3% lower; 2.4x difference).
  • Gemma 4 31B is $0.03 cheaper per 1M output tokens (7.5% lower; 1.08x difference).
  • Gemma 4 31B has 131.07K more context (2x larger).
Head-to-Head Specs
FeatureGemma 4 31B
(Google)
Qwen3 8B
(Qwen)
Input Price
prompt tokens per 1M
$0.12$0.05
Completion Price
per 1M tokens
$0.37$0.4
Sample Workload Cost
1M input + 500K output
$0.3$0.25
Context Window262.14K131.07K
Release Date
Popularity#27#102

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionQwen3 8BOn the standard 1M input plus 500K output workload, Qwen3 8B is estimated at $0.25 vs $0.3 for Gemma 4 31B, saving $0.05 (18% lower).
High-volume input processingQwen3 8BLower 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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Provider catalogs

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Google catalog

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Qwen catalog

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Open Qwen 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...

Qwen3 8B

Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between "thinking" mode for math,...