Qwen3.8 27B vs GLM-5.3-Flash vs Inkling Small
GLM-5.3-Flash comes out ahead, 80 to 70 and 67 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3.8 27B
67/100- ECI149.4
- Price$0.40 / $2.50
- Context262K
- Our pick
Z.ai (Zhipu)GLM-5.3-Flash
80/100- ECI151.9
- Price$0.15 / $0.50
- Context1M
Thinking MachinesInkling Small
70/100- ECI150.2
- Price$0.50 / $1.20
- Context1.05M
GLM-5.3-Flash is our pick
GLM-5.3-Flash is the better all-round choice, scoring 80/100 against Inkling Small (70) and Qwen3.8 27B (67). It leads on capability, price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGLM-5.3-FlashCapabilities Index (ECI): GLM-5.3-Flash 151.9 · Inkling Small 150.2 · Qwen3.8 27B 149.4
- Lowest priceGLM-5.3-FlashGLM-5.3-Flash $0.237 · Inkling Small $0.675 · Qwen3.8 27B $0.925 per 1M tokens (3:1 blend)
- Longest contextInkling SmallInkling Small 1,048,576 · GLM-5.3-Flash 1,000,000 · Qwen3.8 27B 262,144 tokens
- Widest inputsGLM-5.3-FlashQwen3.8 27B: Text, Images, Video · GLM-5.3-Flash: Text, Images, PDFs, Video · Inkling Small: Text, Images, Audio
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3.8 27B | GLM-5.3-Flash | Inkling Small |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 77 | 81 | 78 |
| Price | 25% | 51 | 79 | 58 |
| Inputs & features | 15% | 80 | 90 | 70 |
| Context window | 10% | 37 | 60 | 61 |
| Overall | 100% | 67/100 | 80/100 | 70/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Qwen3.8 27BAlibaba (Qwen) | GLM-5.3-FlashZ.ai (Zhipu) | Inkling SmallThinking Machines |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 149.4 | 151.9 (best) | 150.2 |
| ECI rank | #53 of 148 | #42 of 148 (best) | #47 of 148 |
| GPQA DiamondGraduate-level science questions | — | 90.2% (best) | 88.5% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 55.8% (best) | 46.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 93.9% (best) | 90.0% |
| SimpleQA VerifiedShort factual questions | — | — | 19.1% |
| Price per million tokens | |||
| Input | $0.40 | $0.15 (best) | $0.50 |
| Output | $2.50 | $0.50 (best) | $1.20 |
| Cached input | — | $0.03 | — |
| Blended (3:1) | $0.925 | $0.237 (best) | $0.675 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 39 providers | Official Z.AI API | Median of 11 providers |
| Limits | |||
| Context window | 262,144 tokens | 1,000,000 tokens | 1,048,576 tokens (best) |
| Max output | 32,768 tokens | 131,072 tokens | 1,048,576 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | Yes |
| Video | Yes | Yes | No |
| Reasoning | Yes | Yeslow · high · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Open | Open | OpenApache-2.0 |
| API model ID | — | glm-5.3-flash | — |
| API providers | 41 | 65 (best) | 11 |
| Released | Aug 14, 2026 | Aug 26, 2026 | Jul 30, 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.
- Qwen3.8 27B$9.00
- GLM-5.3-Flash$2.50
- Inkling Small$7.40
Which should you choose?
Which is better: Qwen3.8 27B, GLM-5.3-Flash or Inkling Small?
GLM-5.3-Flash is the better all-round choice, scoring 80/100 against Inkling Small (70) and Qwen3.8 27B (67). It leads on capability, price and inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Qwen3.8 27B, GLM-5.3-Flash or Inkling Small?
GLM-5.3-Flash is cheaper at $0.15 input / $0.50 output per million tokens (official Z.AI API price). Inkling Small costs $0.50 input / $1.20 output per million tokens (median across 11 API providers); Qwen3.8 27B costs $0.40 input / $2.50 output per million tokens (median across 39 API providers). At a typical mix of three input tokens to one output token, that is $0.237 per million tokens for GLM-5.3-Flash versus $0.675 for Inkling Small (2.8× as much) and $0.925 for Qwen3.8 27B (3.9× as much).
Which scores higher on benchmarks?
GLM-5.3-Flash scores higher on the Capabilities Index (ECI): GLM-5.3-Flash 151.9 (#42 of 148), Inkling Small 150.2 (#47 of 148) and Qwen3.8 27B 149.4 (#53 of 148). The confidence ranges of the top two overlap (149.4–154.3 vs 147.5–152.1), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.8 27B, GLM-5.3-Flash and Inkling Small yet, so there is no like-for-like coding score. On overall capability, GLM-5.3-Flash leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Inkling Small has the largest context window at 1,048,576 tokens, against 1,000,000 for GLM-5.3-Flash and 262,144 for Qwen3.8 27B. Maximum output per response: Qwen3.8 27B up to 32,768, GLM-5.3-Flash up to 131,072, Inkling Small up to 1,048,576 tokens.
Which can read images, PDFs, audio or video?
Qwen3.8 27B accepts text, images and video; GLM-5.3-Flash accepts text, images, PDFs and video; Inkling Small accepts text, images and audio. GLM-5.3-Flash handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache-2.0), so you can self-host them.
Which is newer?
GLM-5.3-Flash is the newest, released Aug 26, 2026. Qwen3.8 27B came out Aug 14, 2026; Inkling Small came out Jul 30, 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.
