API cost decision in 10 seconds

Qwen3 32B vs Gemma 4 31B (free)

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

On the standard 1M input plus 500K output workload, Gemma 4 31B (free) is estimated at $0 vs $0.22 for Qwen3 32B, saving $0.22 (100% lower).

Cost-first pickGemma 4 31B (free)
Context-first pickGemma 4 31B (free)
Sample savings$0.22100%
10x traffic gap$2.2

Gemma 4 31B (free) is cheaper on the standard workload and also has the larger context window. At 10x that traffic, the same price gap is about $2.2. 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 (free) stays cheaper across input-heavy, balanced, and output-heavy sample workloads.

Workload shapeToken mixBetter pickQwen3 32BGemma 4 31B (free)
Input-heavy / RAG5M input + 500K outputGemma 4 31B (free)$0.54$0
Balanced workload1M input + 1M outputGemma 4 31B (free)$0.36$0
Output-heavy chatbot1M input + 5M outputGemma 4 31B (free)$1.48$0
Cheaper input Gemma 4 31B (free) $0.08 vs $0 / 1M

Gemma 4 31B (free) is free for input tokens while Qwen3 32B costs $0.08 per 1M tokens.

Cheaper output Gemma 4 31B (free) $0.28 vs $0 / 1M

Gemma 4 31B (free) is free for output tokens while Qwen3 32B costs $0.28 per 1M tokens.

Larger context Gemma 4 31B (free) 131.07K vs 262.14K

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

Sample workload Gemma 4 31B (free) $0.22 vs $0

Gemma 4 31B (free) is free for the standard workload while the other model is estimated at $0.22.

Estimate your workload cost

Your Workload Cost

Prices are normalized to USD per 1M tokens.
Qwen3 32B Calculating… Estimated API cost
Gemma 4 31B (free) 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 (free) has the lower input price; Gemma 4 31B (free) has the lower output price; Gemma 4 31B (free) offers the larger context window. For the 1M input plus 500K output sample, Gemma 4 31B (free) is cheaper for the standard workload.

For a 1M input token plus 500K output token workload, the estimated API cost is $0.22 for Qwen3 32B and $0 for Gemma 4 31B (free).

Best Fit

Choose Qwen3 32B when its provider, model quality, latency, or availability is more important than the numeric price/context winner.

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

Decision Notes
  • On the standard 1M input plus 500K output workload, Gemma 4 31B (free) is estimated at $0 vs $0.22 for Qwen3 32B, saving $0.22 (100% lower).
  • Gemma 4 31B (free) is free for the standard workload while the other model is estimated at $0.22.
  • Gemma 4 31B (free) is free for input tokens while Qwen3 32B costs $0.08 per 1M tokens.
  • Gemma 4 31B (free) is free for output tokens while Qwen3 32B costs $0.28 per 1M tokens.
  • Gemma 4 31B (free) has 131.07K more context (2x larger).
Head-to-Head Specs
FeatureQwen3 32B
(Qwen)
Gemma 4 31B (free)
(Google)
Input Price
prompt tokens per 1M
$0.08$0
Completion Price
per 1M tokens
$0.28$0
Sample Workload Cost
1M input + 500K output
$0.22$0
Context Window131.07K262.14K
Release Date
Popularity#93#105

Use-Case Decision Matrix

Use caseBetter pickWhy
Budget-constrained productionGemma 4 31B (free)On the standard 1M input plus 500K output workload, Gemma 4 31B (free) is estimated at $0 vs $0.22 for Qwen3 32B, saving $0.22 (100% lower).
High-volume input processingGemma 4 31B (free)Lower prompt-token price matters most when prompts, retrieved passages, or documents dominate the bill.
Long responses and chatbotsGemma 4 31B (free)Lower output-token price matters most when assistants generate many completion tokens.
RAG or long-document workGemma 4 31B (free)A larger context window leaves more room for retrieved passages, conversation history, or source files.

Related Alternatives

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

Qwen catalog

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

Open Qwen models

Google catalog

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

Open Google models
Qwen3 32B

Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for...

Gemma 4 31B (free)

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...