AI model comparison
Qwen3.5-122B-A10B vs GLM 4.6
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency.
- Input / 1M
- $0.26
- Output / 1M
- $2.08
- Context
- 262K
Z.ai
GLM 4.6
Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex
- Input / 1M
- $0.43
- Output / 1M
- $1.75
- Context
- 205K
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
Qwen3.5-122B-A10B
1.1x cheaper for a typical chat
Higher intelligence score
GLM 4.6
18.5 vs 17.7 on Artificial Analysis
Larger context window
Qwen3.5-122B-A10B
262K vs 205K tokens
Newer release
Qwen3.5-122B-A10B
Released February 25, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Qwen3.5-122B-A10B17.7Coding index
Qwen3.5-122B-A10B43.3Agentic index
Qwen3.5-122B-A10BNot availablePricing
Qwen3.5-122B-A10B vs GLM 4.6 API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | Qwen3.5-122B-A10B | |
|---|---|---|
| Input tokensPer 1M tokens | $0.26 | $0.43 |
| Output tokensPer 1M tokens | $2.08 | $1.75 |
| Cached input (read)Per 1M tokens | Not available | $0.08 |
| 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
Qwen3.5-122B-A10B$1.56Coding task
30K in, 4K out
Qwen3.5-122B-A10B$16.12Long document summary
150K in, 2K out
Qwen3.5-122B-A10B$43.16Specs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | Qwen3.5-122B-A10B | |
|---|---|---|
| Context window | 262K tokens | 205K tokens |
| Max output | 66K tokens | 16K tokens |
| Input types | Image, Text, Video | Text |
| Output types | Text | Text |
| Reasoning effort levels | Not available | Not available |
| Default reasoning effort | Not available | Not available |
| Release date | February 25, 2026 | September 30, 2025 |
Features
Supported features
API features available for each model.
| Metric | Qwen3.5-122B-A10B | |
|---|---|---|
| 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.
Qwen3.5-122B-A10BView full model details- Qwen3.5-122B-A10B vs Qwen3.5 397B A17B
- Qwen3.5-122B-A10B vs Qwen3.6 27B
- Qwen3.5-122B-A10B vs GPT-5 Mini
- Qwen3.5-122B-A10B vs Qwen3.5-35B-A3B
- Qwen3.5-122B-A10B vs Qwen3.6 35B A3B
- Qwen3.5-122B-A10B vs Muse Glimmer 30B
- Qwen3.5-122B-A10B vs Gemini 3.1 Flash Lite
- Qwen3.5-122B-A10B vs Claude Haiku 4.5
- Qwen3.5-122B-A10B vs Gemini 2.5 Flash
- Qwen3.5-122B-A10B vs Kimi K2 0905
- GLM 4.6 vs DeepSeek V4 Flash 0423
- GLM 4.6 vs Gemini 3.1 Flash Lite
- GLM 4.6 vs Gemini 2.5 Flash
- GLM 4.6 vs GLM 4.7
- GLM 4.6 vs Claude Haiku 4.5
- GLM 4.6 vs GPT-5 Mini
- GLM 4.6 vs GPT-5.1-Codex-Mini
- GLM 4.6 vs GLM 5.3 FlashX
- GLM 4.6 vs Step 3.7 Flash
- GLM 4.6 vs LongCat 2.0
- GLM 4.6 vs MiniMax M2
- GLM 4.6 vs Qwen3.6 35B A3B
- GLM 4.6 vs Muse Glimmer 30B
Our take
Qwen3.5-122B-A10B is 1.1x cheaper for a typical chat. GLM 4.6 scores higher on the Artificial Analysis Intelligence Index (18.5 vs 17.7). Qwen3.5-122B-A10B has the larger context window (262K vs 205K 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 Qwen3.5-122B-A10B or GLM 4.6 cheaper?
Qwen3.5-122B-A10B is cheaper for a typical chat (2K input tokens and 500 output tokens). Qwen3.5-122B-A10B costs $0.26 per 1M input tokens and $2.08 per 1M output tokens, while GLM 4.6 costs $0.43 per 1M input tokens and $1.75 per 1M output tokens.
Which has a bigger context window, Qwen3.5-122B-A10B or GLM 4.6?
Qwen3.5-122B-A10B supports up to 262K tokens of context, compared with 205K for GLM 4.6.
Is Qwen3.5-122B-A10B smarter than GLM 4.6?
On the Artificial Analysis Intelligence Index, Qwen3.5-122B-A10B scores 17.7 and GLM 4.6 scores 18.5, putting GLM 4.6 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use Qwen3.5-122B-A10B and GLM 4.6 at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see Qwen3.5-122B-A10B and GLM 4.6 answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to Qwen3.5-122B-A10B and GLM 4.6 at the same time. Compare the answers side by side and keep the best one.