AI model comparison
Mercury 2.5 vs Qwen3.5-9B
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Inception
Mercury 2.5
Mercury 2.5 is the fastest reasoning LLM, and the latest diffusion LLM (dLLM) from Inception.
- Input / 1M
- $0.04
- Output / 1M
- $0.15
- Context
- 260K
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
Mercury 2.5
1.8x cheaper for a typical chat
Higher intelligence score
Qwen3.5-9B
13.3 vs 12.3 on Artificial Analysis
Larger context window
Qwen3.5-9B
262K vs 260K tokens
Newer release
Mercury 2.5
Released September 8, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Mercury 2.512.3Qwen3.5-9B13.3Coding index
Mercury 2.5Not availableQwen3.5-9B23.5Agentic index
Mercury 2.5Not availableQwen3.5-9BNot availablePricing
Mercury 2.5 vs Qwen3.5-9B API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | Mercury 2.5 | Qwen3.5-9B |
|---|---|---|
| Input tokensPer 1M tokens | $0.04 | $0.10 |
| Output tokensPer 1M tokens | $0.15 | $0.15 |
| Cached input (read)Per 1M tokens | $0.00 | 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
Mercury 2.5$0.16Qwen3.5-9B$0.27Coding task
30K in, 4K out
Mercury 2.5$1.80$0.72 cachedQwen3.5-9B$3.60Long document summary
150K in, 2K out
Mercury 2.5$6.30$0.90 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 | Mercury 2.5 | Qwen3.5-9B |
|---|---|---|
| Context window | 260K tokens | 262K tokens |
| Max output | 66K tokens | 33K tokens |
| Input types | Text | Image, Text, Video |
| Output types | Text | Text |
| Reasoning effort levels | High, Medium, Low, None | Not available |
| Default reasoning effort | Medium | Not available |
| Release date | September 8, 2026 | March 10, 2026 |
Features
Supported features
API features available for each model.
| Metric | Mercury 2.5 | 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 | Not 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.
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 Gemma 4 31B
- 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 Nemotron 3.5 Lightning
- Qwen3.5-9B vs Qwen3 32B
- Qwen3.5-9B vs Qwen3 Coder 30B A3B Instruct
Our take
Mercury 2.5 is 1.8x cheaper for a typical chat. Qwen3.5-9B scores higher on the Artificial Analysis Intelligence Index (13.3 vs 12.3). Qwen3.5-9B has the larger context window (262K vs 260K tokens). 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 Mercury 2.5 or Qwen3.5-9B cheaper?
Mercury 2.5 is cheaper for a typical chat (2K input tokens and 500 output tokens). Mercury 2.5 costs $0.04 per 1M input tokens and $0.15 per 1M output tokens, while Qwen3.5-9B costs $0.10 per 1M input tokens and $0.15 per 1M output tokens.
Which has a bigger context window, Mercury 2.5 or Qwen3.5-9B?
Qwen3.5-9B supports up to 262K tokens of context, compared with 260K for Mercury 2.5.
Is Mercury 2.5 smarter than Qwen3.5-9B?
On the Artificial Analysis Intelligence Index, Mercury 2.5 scores 12.3 and Qwen3.5-9B scores 13.3, putting Qwen3.5-9B ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Mercury 2.5 and Qwen3.5-9B at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Mercury 2.5 and Qwen3.5-9B answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Mercury 2.5 and Qwen3.5-9B at the same time. Compare the answers side by side and keep the best one.