Google: Gemma 4 31B vs Qwen: Qwen3.7 Plus (batch)

Neither wins outright: Google: Gemma 4 31B is the pick when cost matters at scale, Qwen: Qwen3.7 Plus (batch) when you generate long outputs in one go.

Key numbers

SpecGoogle: Gemma 4 31BQwen: Qwen3.7 Plus (batch)
ProviderGoogleQwen
Input price (per 1M tokens)$0.20$0.23
Output price (per 1M tokens)$0.40$0.92
Blended price (3:1 input/output)$0.25$0.40
Cheaper than … of all models we track66%57%
Context window262K tokens (~393 pages)262K tokens (~393 pages)
Larger context than … of all models48%48%
Max output per response16K tokens (~12,300 words)131K tokens (~98,300 words)
Input typesimage, text, videotext, image
Output typestexttext
Free variantYesNo
Listed since2026-04-022026-06-03

What it costs for real workloads

Monthly cost at list price for five common workloads. Token counts per unit are stated so you can scale them to your own traffic. A workload that does not fit a model's context window is marked rather than priced.

Workload (per month)Google: Gemma 4 31BQwen: Qwen3.7 Plus (batch)Difference
Customer-support chatbot
50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens)
$23.00$35.65Google: Gemma 4 31B saves $12.65
RAG search over documents
20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens)
$28.00$36.80Google: Gemma 4 31B saves $8.80
Coding / tool-using agent
2,000 tasks — each a multi-step task with tool calls and file context (60,000 in / 6,000 out tokens)
$28.80$38.64Google: Gemma 4 31B saves $9.84
Batch summarisation
10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens)
$18.40$23.92Google: Gemma 4 31B saves $5.52
Long-document analysis
500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens)
$10.40$12.42Google: Gemma 4 31B saves $2.02

Capabilities side by side

CapabilityGoogle: Gemma 4 31BQwen: Qwen3.7 Plus (batch)What it lets you do
Tool / function calling✓ Yes✓ Yescall your APIs and run agent loops
Structured output (JSON schema)✓ Yes✓ Yesreturn JSON that validates against your schema
Reasoning / thinking mode✓ Yes✓ Yesspend extra tokens thinking before answering hard problems
Image input✓ Yes✓ Yesread screenshots, charts and scanned pages
Audio input——take speech or audio directly
File / PDF input——accept documents without your own parsing step
Image output——generate images, not just text
Built-in web search——answer from live web results
Seed (reproducible sampling)✓ Yes✓ Yesrepeat a generation for tests and evals
Log-probabilities✓ Yes✓ Yesread token confidence for classification and scoring

From each model's published parameters and input/output types. “—” means not advertised, not proven absent.

Price history

DateModelInput / 1MOutput / 1M
2026-10-15Google: Gemma 4 31B$0.20$0.40
2026-08-27Google: Gemma 4 31B$0.09$0.34
2026-08-21Google: Gemma 4 31B$0.10$0.34
2026-08-19Google: Gemma 4 31B$0.09$0.34

Which should you choose?

Choose Google: Gemma 4 31B if…

  • cost matters at scale — it is 38% cheaper on a typical 3:1 input/output mix ($23.00 vs $35.65 a month for 50,000 support chats)
  • you want to prototype at zero cost — a free variant is listed

Choose Qwen: Qwen3.7 Plus (batch) if…

  • you generate long outputs in one go — up to 131K tokens (about 98,300 words) per response

Frequently asked questions

Is Google: Gemma 4 31B better than Qwen: Qwen3.7 Plus (batch)?

Neither wins outright: Google: Gemma 4 31B is the pick when cost matters at scale, Qwen: Qwen3.7 Plus (batch) when you generate long outputs in one go. In short: Google: Gemma 4 31B when cost matters at scale, you want to prototype at zero cost; Qwen: Qwen3.7 Plus (batch) when you generate long outputs in one go.

Which is cheaper, Google: Gemma 4 31B or Qwen: Qwen3.7 Plus (batch)?

Google: Gemma 4 31B is cheaper: $0.25 vs $0.40 per 1M tokens at a typical 3:1 input/output mix, 38% less. For 50,000 support conversations a month that is $23.00 against $35.65.

How much do Google: Gemma 4 31B and Qwen: Qwen3.7 Plus (batch) cost per million tokens?

Google: Gemma 4 31B costs $0.20 per 1M input tokens and $0.40 per 1M output tokens; Qwen: Qwen3.7 Plus (batch) costs $0.23 input and $0.92 output. Output tokens are billed separately, so long answers shift the gap.

Which has the bigger context window, Google: Gemma 4 31B or Qwen: Qwen3.7 Plus (batch)?

Both take 262K tokens per request — roughly 393 pages of text.

Do Google: Gemma 4 31B and Qwen: Qwen3.7 Plus (batch) support function calling and JSON output?

Tool calling — Google: Gemma 4 31B: yes, Qwen: Qwen3.7 Plus (batch): yes. JSON-schema structured output — Google: Gemma 4 31B: yes, Qwen: Qwen3.7 Plus (batch): yes.

Can Google: Gemma 4 31B or Qwen: Qwen3.7 Plus (batch) read images?

Yes, both accept image input.

Is Google: Gemma 4 31B or Qwen: Qwen3.7 Plus (batch) free to use?

Google: Gemma 4 31B has a free variant (google/gemma-4-31b-it:free). Free variants usually carry tighter rate limits.

What is the maximum output length of Google: Gemma 4 31B and Qwen: Qwen3.7 Plus (batch)?

Google: Gemma 4 31B can write up to 16K tokens (about 12,300 words) per response; Qwen: Qwen3.7 Plus (batch) up to 131K (about 98,300 words).

When were Google: Gemma 4 31B and Qwen: Qwen3.7 Plus (batch) released?

Google: Gemma 4 31B was listed on 2026-04-02; Qwen: Qwen3.7 Plus (batch) on 2026-06-03.

Has the price of Google: Gemma 4 31B or Qwen: Qwen3.7 Plus (batch) changed?

Google: Gemma 4 31B: input price raised 55% ($0.09 → $0.20/1M), output price raised 15% ($0.34 → $0.40/1M) on 2026-10-15 (4 price changes since 2026-08-06). Qwen: Qwen3.7 Plus (batch): price unchanged since we started tracking it on 2026-10-17.

About the two models

Google

Google: 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...

Full Google: Gemma 4 31B specs →

Compare Google: Gemma 4 31B with other models

Compare Qwen: Qwen3.7 Plus (batch) with other models

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