AI model comparison
GLM 5.3 vs Qwen3.8 2.4T A95B
Pricing, context window, benchmarks, and features compared side by side. Or skip the guesswork and send one prompt to both.
Z.ai
GLM 5.3
GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks.
- Input / 1M
- $0.04
- Output / 1M
- $7
- Context
- 1.05M
Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out of 2.4 trillion total.
- Input / 1M
- $2
- Output / 1M
- $6
- Context
- 1.05M
At a glance
Quick verdict
How the two models stack up on the things people ask about most.
Lower price
GLM 5.3
2.0x cheaper for a typical chat
Higher intelligence score
GLM 5.3
44.8 vs 39.9 on Artificial Analysis
Larger context window
Tie
1.05M vs 1.05M tokens
Newer release
GLM 5.3
Released August 18, 2026
Benchmarks
Benchmark scores
Independent scores from Artificial Analysis. Higher is better.
Intelligence index
Qwen3.8 2.4T A95B39.9Coding index
Qwen3.8 2.4T A95B71.9Agentic index
Qwen3.8 2.4T A95B50.1Pricing
GLM 5.3 vs Qwen3.8 2.4T A95B API pricing
Per-token API rates. Cheaper option highlighted.
| Metric | Qwen3.8 2.4T A95B | |
|---|---|---|
| Input tokensPer 1M tokens | $0.04 | $2 |
| Output tokensPer 1M tokens | $7 | $6 |
| Cached input (read)Per 1M tokens | $0.04 | $0.25 |
| 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
Qwen3.8 2.4T A95B$7Coding task
30K in, 4K out
Qwen3.8 2.4T A95B$84$31.50 cachedLong document summary
150K in, 2K out
Qwen3.8 2.4T A95B$312$49.50 cachedSpecs
Context window and capabilities
How much each model can read, how much it can write, and what it accepts as input.
| Metric | Qwen3.8 2.4T A95B | |
|---|---|---|
| Context window | 1.05M tokens | 1.05M tokens |
| Max output | 944K tokens | 131K tokens |
| Input types | Text | Text |
| Output types | Text | Text |
| Reasoning effort levels | Max, High, Low | Extra high, Medium, Low |
| Default reasoning effort | Max | Extra high |
| Release date | August 18, 2026 | August 12, 2026 |
Features
Supported features
API features available for each model.
| Metric | Qwen3.8 2.4T A95B | |
|---|---|---|
| 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.
- GLM 5.3 vs DeepSeek V4.1 Flash
- GLM 5.3 vs GLM 5.3 Flash
- GLM 5.3 vs MiMo-V2.6-Flash
- GLM 5.3 vs Hy4 preview
- GLM 5.3 vs GPT-6 Luna
- GLM 5.3 vs Claude Opus 5.5
- GLM 5.3 vs DeepSeek V4 Flash 0731
- GLM 5.3 vs Claude Sonnet 5.5
- GLM 5.3 vs GPT-6 Astra
- GLM 5.3 vs DeepSeek V4 Flash 0423
- GLM 5.3 vs GPT-6.1 Sol
- GLM 5.3 vs Step 5 Preview
- GLM 5.3 vs Gemini 3.8 Flash
- GLM 5.3 vs Hy3
- GLM 5.3 vs Muse Spark 1.3
- GLM 5.3 vs GPT-6 Sol
- GLM 5.3 vs Kimi K3
- GLM 5.3 vs GPT-5.6 Sol
- GLM 5.3 vs Grok 4.7
- GLM 5.3 vs MiMo-V2.6-Pro
- GLM 5.3 vs Qwen3.8 Max (0902)
- GLM 5.3 vs GLM 5.2
- GLM 5.3 vs Grok 4.6
- GLM 5.3 vs Claude Haiku 5.5
- GLM 5.3 vs GPT-5.6 Terra
- GLM 5.3 vs Claude Opus 4.8
- GLM 5.3 vs Ling 3.1 Flash
- GLM 5.3 vs Claude Opus 4.7
- GLM 5.3 vs Gemini 3.7 Flash
- GLM 5.3 vs Muse Spark 1.2
- GLM 5.3 vs Grok 4.5
Qwen3.8 2.4T A95BView full model details- Qwen3.8 2.4T A95B vs Qwen3.8 Max (0902)
- Qwen3.8 2.4T A95B vs Kimi K3
- Qwen3.8 2.4T A95B vs GPT-5.6 Terra
- Qwen3.8 2.4T A95B vs Gemini 3.8 Flash
- Qwen3.8 2.4T A95B vs Gemini 3.7 Flash
- Qwen3.8 2.4T A95B vs Muse Spark 1.2
- Qwen3.8 2.4T A95B vs DeepSeek V4.1 Flash
- Qwen3.8 2.4T A95B vs Grok 4.5
- Qwen3.8 2.4T A95B vs Claude Sonnet 5
Our take
GLM 5.3 is 2.0x cheaper for a typical chat. GLM 5.3 scores higher on the Artificial Analysis Intelligence Index (44.8 vs 39.9). 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 GLM 5.3 or Qwen3.8 2.4T A95B cheaper?
GLM 5.3 is cheaper for a typical chat (2K input tokens and 500 output tokens). GLM 5.3 costs $0.04 per 1M input tokens and $7 per 1M output tokens, while Qwen3.8 2.4T A95B costs $2 per 1M input tokens and $6 per 1M output tokens.
Which has a bigger context window, GLM 5.3 or Qwen3.8 2.4T A95B?
GLM 5.3 supports up to 1.05M tokens of context, compared with 1.05M for Qwen3.8 2.4T A95B.
Is GLM 5.3 smarter than Qwen3.8 2.4T A95B?
On the Artificial Analysis Intelligence Index, GLM 5.3 scores 44.8 and Qwen3.8 2.4T A95B scores 39.9, putting GLM 5.3 ahead. Benchmarks don't capture everything, so the best test is running your own prompts through both.
Can I use GLM 5.3 and Qwen3.8 2.4T A95B at the same time?
Yes. Shortcut Chat sends one prompt to multiple models at once, so you can see GLM 5.3 and Qwen3.8 2.4T A95B answer side by side and keep the better response.
Why choose? Ask both.
Send one prompt to GLM 5.3 and Qwen3.8 2.4T A95B at the same time. Compare the answers side by side and keep the best one.