GLM-5.3 vs Qwen3.8 27B
Qwen3.8 27B comes out ahead, 67 to 64 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5.3
64/100- ECI155.8
- Price$1.40 / $4.40
- Context1M
- Our pick
Alibaba (Qwen)Qwen3.8 27B
67/100- ECI149.4
- Price$0.40 / $2.50
- Context262K
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Qwen3.8 27B is our pick
Qwen3.8 27B is the better all-round choice, scoring 67/100 against GLM-5.3 (64). It leads on price and inputs & features. GLM-5.3 wins on capability and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-5.3Capabilities Index (ECI): GLM-5.3 155.8 · Qwen3.8 27B 149.4
- Lowest priceQwen3.8 27BQwen3.8 27B $0.925 · GLM-5.3 $2.15 per 1M tokens (3:1 blend)
- Longest contextGLM-5.3GLM-5.3 1,000,000 · Qwen3.8 27B 262,144 tokens
- Widest inputsQwen3.8 27BGLM-5.3: Text · Qwen3.8 27B: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5.3 | Qwen3.8 27B |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 85 | 77 |
| Price | 25% | 34 | 51 |
| Inputs & features | 15% | 45 | 80 |
| Context window | 10% | 60 | 37 |
| Overall | 100% | 64/100 | 67/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5.3Z.ai (Zhipu) | Qwen3.8 27BAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 155.8 (best) | 149.4 |
| ECI rank | #24 of 148 (best) | #53 of 148 |
| GPQA DiamondGraduate-level science questions | 90.9% | — |
| FrontierMath Tiers 1–3Research-level mathematics | 68.8% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 91.1% | — |
| SimpleQA VerifiedShort factual questions | 41.0% | — |
| Price per million tokens | ||
| Input | $1.40 | $0.40 (best) |
| Output | $4.40 | $2.50 (best) |
| Cached input | $0.26 | — |
| Blended (3:1) | $2.15 | $0.925 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 39 providers |
| Limits | ||
| Context window | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens (best) | 32,768 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | Yes |
| Reasoning | Yeslow · high · max | Yes |
| Tool calling | Yes | Yes |
| Structured output | Yes | Yes |
| Availability | ||
| Weights | Open | Open |
| API model ID | glm-5.3 | — |
| API providers | 62 (best) | 41 |
| Released | Aug 14, 2026 | Aug 14, 2026 |
| Knowledge cutoff | — | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-5.3$22.80
- Qwen3.8 27B$9.00
Which should you choose?
Which is better: GLM-5.3 or Qwen3.8 27B?
Qwen3.8 27B is the better all-round choice, scoring 67/100 against GLM-5.3 (64). It leads on price and inputs & features. GLM-5.3 wins on capability and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5.3 or Qwen3.8 27B?
Qwen3.8 27B is cheaper at $0.40 input / $2.50 output per million tokens (median across 39 API providers). GLM-5.3 costs $1.40 input / $4.40 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.925 per million tokens for Qwen3.8 27B versus $2.15 for GLM-5.3 (2.3× as much).
Which scores higher on benchmarks?
GLM-5.3 scores higher on the Capabilities Index (ECI): GLM-5.3 155.8 (#24 of 148) and Qwen3.8 27B 149.4 (#53 of 148). Their confidence ranges do not overlap (153.7–158.3 vs 147.5–151.6), so the gap is a real one.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5.3 and Qwen3.8 27B yet, so there is no like-for-like coding score. On overall capability, GLM-5.3 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
GLM-5.3 has the largest context window at 1,000,000 tokens, against 262,144 for Qwen3.8 27B. Maximum output per response: GLM-5.3 up to 131,072, Qwen3.8 27B up to 32,768 tokens.
Which can read images, PDFs, audio or video?
GLM-5.3 accepts text; Qwen3.8 27B accepts text, images and video. Qwen3.8 27B handles the widest range of inputs.
Are any of these open source?
Yes, both publish their weights, so you can self-host them.
Which is newer?
GLM-5.3 is the newest, released Aug 14, 2026. Qwen3.8 27B came out Aug 14, 2026.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Oct 4, 2026.
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