Choose Google: Gemma 4 31B (batch) if…
No advantage on the data we track — pick it for provider preference or output quality on your own prompts.
Google: Gemma 4 31B (batch) vs Qwen: Qwen3.5-9B (batch)
Qwen: Qwen3.5-9B (batch) comes out ahead on the data we track: 1 advantage and none for Google: Gemma 4 31B (batch).
Key numbers
| Spec | Google: Gemma 4 31B (batch) | Qwen: Qwen3.5-9B (batch) |
|---|---|---|
| Provider | Qwen | |
| Input price (per 1M tokens) | $0.39 | $0.17 |
| Output price (per 1M tokens) | $0.97 | $0.25 |
| Blended price (3:1 input/output) | $0.53 | $0.19 |
| Cheaper than … of all models we track | 52% | 70% |
| Context window | 262K tokens (~393 pages) | 262K tokens (~393 pages) |
| Larger context than … of all models | 47% | 47% |
| Max output per response | 236K tokens (~176,900 words) | 236K tokens (~176,900 words) |
| Input types | image, text, video | text, image, video |
| Output types | text | text |
| Free variant | No | No |
| Listed since | 2026-04-02 | 2026-03-10 |
Advertisement
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 31B (batch) | Qwen: Qwen3.5-9B (batch) | Difference |
|---|---|---|---|
| Customer-support chatbot 50,000 conversations — each a 6-turn conversation (1,500 in / 400 out tokens) | $48.65 | $17.75 | Qwen: Qwen3.5-9B (batch) saves $30.90 |
| RAG search over documents 20,000 questions — each a question answered from ~8 retrieved passages (6,000 in / 500 out tokens) | $56.50 | $22.90 | Qwen: Qwen3.5-9B (batch) saves $33.60 |
| 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) | $58.44 | $23.40 | Qwen: Qwen3.5-9B (batch) saves $35.04 |
| Batch summarisation 10,000 documents — each a 6,000-word report summarised to a page (8,000 in / 600 out tokens) | $37.02 | $15.10 | Qwen: Qwen3.5-9B (batch) saves $21.92 |
| Long-document analysis 500 documents — each a 150-page contract or codebase read in one pass (100,000 in / 2,000 out tokens) | $20.47 | $8.75 | Qwen: Qwen3.5-9B (batch) saves $11.72 |
Capabilities side by side
| Capability | Google: Gemma 4 31B (batch) | Qwen: Qwen3.5-9B (batch) | What it lets you do |
|---|---|---|---|
| Tool / function calling | ✓ Yes | ✓ Yes | call your APIs and run agent loops |
| Structured output (JSON schema) | ✓ Yes | ✓ Yes | return JSON that validates against your schema |
| Reasoning / thinking mode | ✓ Yes | ✓ Yes | spend extra tokens thinking before answering hard problems |
| Image input | ✓ Yes | ✓ Yes | read 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) | — | — | repeat a generation for tests and evals |
| Log-probabilities | — | — | read token confidence for classification and scoring |
From each model's published parameters and input/output types. “—” means not advertised, not proven absent.
Price history
- Google: Gemma 4 31B (batch): price unchanged since we started tracking it on 2026-08-29.
- Qwen: Qwen3.5-9B (batch): price unchanged since we started tracking it on 2026-08-29.
Which should you choose?
Choose Qwen: Qwen3.5-9B (batch) if…
- cost matters at scale — it is 64% cheaper on a typical 3:1 input/output mix ($17.75 vs $48.65 a month for 50,000 support chats)
Frequently asked questions
Is Google: Gemma 4 31B (batch) better than Qwen: Qwen3.5-9B (batch)?
Qwen: Qwen3.5-9B (batch) comes out ahead on the data we track: 1 advantage and none for Google: Gemma 4 31B (batch).
Which is cheaper, Google: Gemma 4 31B (batch) or Qwen: Qwen3.5-9B (batch)?
Qwen: Qwen3.5-9B (batch) is cheaper: $0.19 vs $0.53 per 1M tokens at a typical 3:1 input/output mix, 64% less. For 50,000 support conversations a month that is $17.75 against $48.65.
How much do Google: Gemma 4 31B (batch) and Qwen: Qwen3.5-9B (batch) cost per million tokens?
Google: Gemma 4 31B (batch) costs $0.39 per 1M input tokens and $0.97 per 1M output tokens; Qwen: Qwen3.5-9B (batch) costs $0.17 input and $0.25 output. Output tokens are billed separately, so long answers shift the gap.
Which has the bigger context window, Google: Gemma 4 31B (batch) or Qwen: Qwen3.5-9B (batch)?
Both take 262K tokens per request — roughly 393 pages of text.
Do Google: Gemma 4 31B (batch) and Qwen: Qwen3.5-9B (batch) support function calling and JSON output?
Tool calling — Google: Gemma 4 31B (batch): yes, Qwen: Qwen3.5-9B (batch): yes. JSON-schema structured output — Google: Gemma 4 31B (batch): yes, Qwen: Qwen3.5-9B (batch): yes.
Can Google: Gemma 4 31B (batch) or Qwen: Qwen3.5-9B (batch) read images?
Yes, both accept image input.
Is Google: Gemma 4 31B (batch) or Qwen: Qwen3.5-9B (batch) free to use?
No free variant is listed for either; both are pay-per-token.
What is the maximum output length of Google: Gemma 4 31B (batch) and Qwen: Qwen3.5-9B (batch)?
Google: Gemma 4 31B (batch) can write up to 236K tokens (about 176,900 words) per response; Qwen: Qwen3.5-9B (batch) up to 236K (about 176,900 words).
When were Google: Gemma 4 31B (batch) and Qwen: Qwen3.5-9B (batch) released?
Google: Gemma 4 31B (batch) was listed on 2026-04-02; Qwen: Qwen3.5-9B (batch) on 2026-03-10.
About the two models
Google: Gemma 4 31B (batch)
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 (batch) specs →Qwen: Qwen3.5-9B (batch)
Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...
Full Qwen: Qwen3.5-9B (batch) specs →