AI model comparison
Gemma 4 31B vs Qwen3.5-9B
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Gemma 4 31B
Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output.
- Input / 1M
- $0.09
- Output / 1M
- $0.34
- Context
- 262K
Qwen
Qwen3.5-9B
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.
Qwen3.5-9B is scheduled to be retired on October 21, 2026. Consider Qwen3.8 27B instead.
- Input / 1M
- $0.10
- Output / 1M
- $0.15
- Context
- 262K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Qwen3.5-9B
1.3x cheaper for a typical chat
Higher intelligence score
Gemma 4 31B
14.7 vs 13.3 on Artificial Analysis
Larger context window
Tie
262K vs 262K tokens
Newer release
Gemma 4 31B
Released April 2, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Gemma 4 31B14.7Qwen3.5-9B13.3Coding index
Gemma 4 31B43.4Qwen3.5-9B23.5Agentic index
Gemma 4 31B4.2Qwen3.5-9BNot availablePricing
Gemma 4 31B vs Qwen3.5-9B API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | Gemma 4 31B | Qwen3.5-9B |
|---|---|---|
| Input tokensPer 1M tokens | $0.09 | $0.10 |
| Output tokensPer 1M tokens | $0.34 | $0.15 |
| Cached input (read)Per 1M tokens | $0.05 | Not available |
| Cache writePer 1M tokens | Not available | Not available |
What it costs in practice
Estimated cost per 1,000 requests at standard rates. Coding and long-document figures also show the cost when the input is already cached.
Chat message
2K in, 500 out
Gemma 4 31B$0.35Qwen3.5-9B$0.27Coding task
30K in, 4K out
Gemma 4 31B$4.06$2.86 cachedQwen3.5-9B$3.60Long document summary
150K in, 2K out
Gemma 4 31B$14.18$8.18 cachedQwen3.5-9B$15.30Specs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | Gemma 4 31B | Qwen3.5-9B |
|---|---|---|
| Context window | 262K tokens | 262K tokens |
| Max output | 16K tokens | 33K tokens |
| Input types | Image, Text, Video | Image, Text, Video |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | April 2, 2026 | March 10, 2026 |
Features
Supported features
API features available for each model.
| Metric | Gemma 4 31B | Qwen3.5-9B |
|---|---|---|
| Tool calling | Supported | Supported |
| Structured outputs | Supported | Supported |
| JSON mode | Supported | Supported |
| Reasoning | Supported | Supported |
| Temperature | Supported | Supported |
| Stop sequences | Supported | Supported |
| Deterministic seed | Supported | Supported |
| Verbosity control | Not supported | Not supported |
Keep exploring
More on these models
Other head-to-heads, the full model page, and the alternatives list for each model.
Gemma 4 31BView full model details- Gemma 4 31B vs Gemini 2.5 Flash Lite
- Gemma 4 31B vs gpt-oss-120b
- Gemma 4 31B vs Gemini 3.1 Flash Lite
- Gemma 4 31B vs Gemini 2.5 Flash
- Gemma 4 31B vs Gemma 4 26B A4B
- Gemma 4 31B vs GPT-5 Nano
- Gemma 4 31B vs Ling 3.0 Flash
- Gemma 4 31B vs DeepSeek V3.1
- Gemma 4 31B vs Mercury 2.5
- Gemma 4 31B vs Nemotron 3.5 Lightning
- Gemma 4 31B vs Granite 4.2 8B
- Gemma 4 31B vs GLM 4.7 Flash
- Gemma 4 31B vs DeepSeek V3.2 Exp
- Gemma 4 31B vs Nemotron 3 Super
- Gemma 4 31B vs Step 3.5 Flash
- Gemma 4 31B vs DeepSeek V3.1 Terminus
- Gemma 4 31B vs Mercury 2
Qwen3.5-9BView full model details- Qwen3.5-9B vs Gemini 2.5 Flash Lite
- Qwen3.5-9B vs gpt-oss-120b
- Qwen3.5-9B vs Qwen3.7 Flash
- Qwen3.5-9B vs gpt-oss-20b
- Qwen3.5-9B vs GPT-5 Nano
- Qwen3.5-9B vs Qwen3 235B A22B Instruct 2507
- Qwen3.5-9B vs Mercury 2.5
- Qwen3.5-9B vs Nemotron 3.5 Lightning
- Qwen3.5-9B vs Qwen3 32B
- Qwen3.5-9B vs Qwen3 Coder 30B A3B Instruct
Our take
Qwen3.5-9B is 1.3x cheaper for a typical chat. Gemma 4 31B scores higher on the Artificial Analysis Intelligence Index (14.7 vs 13.3). The right pick depends on your workload, so the quickest way to settle it is to send the same prompt to both and compare.
FAQ
Frequently asked questions
Is Gemma 4 31B or Qwen3.5-9B cheaper?
Qwen3.5-9B is cheaper for a typical chat (2K input tokens and 500 output tokens). Qwen3.5-9B costs $0.10 per 1M input tokens and $0.15 per 1M output tokens, while Gemma 4 31B costs $0.09 per 1M input tokens and $0.34 per 1M output tokens.
Which has a bigger context window, Gemma 4 31B or Qwen3.5-9B?
Gemma 4 31B supports up to 262K tokens of context, compared with 262K for Qwen3.5-9B.
Is Gemma 4 31B smarter than Qwen3.5-9B?
On the Artificial Analysis Intelligence Index, Gemma 4 31B scores 14.7 and Qwen3.5-9B scores 13.3, putting Gemma 4 31B ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Gemma 4 31B and Qwen3.5-9B at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Gemma 4 31B and Qwen3.5-9B answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Gemma 4 31B and Qwen3.5-9B at the same time. Compare the answers side by side and keep the best one.