GLM-5 vs Inkling Small vs Qwen3.6 27B
Inkling Small comes out ahead, 70 to 65 and 55 on our weighted score, and it is the cheaper option too.
Z.ai (Zhipu)GLM-5
55/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
- Our pick
Thinking MachinesInkling Small
70/100- ECI150.2
- Price$0.50 / $1.20
- Context1.05M
Alibaba (Qwen)Qwen3.6 27B
65/100- ECI146.5
- Price$0.60 / $3.60
- Context262K
Inkling Small is our pick
Inkling Small is the better all-round choice, scoring 70/100 against Qwen3.6 27B (65) and GLM-5 (55). It leads on capability, price and context window. Qwen3.6 27B wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityInkling SmallCapabilities Index (ECI): Inkling Small 150.2 · Qwen3.6 27B 146.5 · GLM-5 145.8
- Lowest priceInkling SmallInkling Small $0.675 · Qwen3.6 27B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextInkling SmallInkling Small 1,048,576 · Qwen3.6 27B 262,144 · GLM-5 204,800 tokens
- Widest inputsQwen3.6 27BGLM-5: Text · Inkling Small: Text, Images, Audio · Qwen3.6 27B: Text, Images, Audio, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5 | Inkling Small | Qwen3.6 27B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 78 | 74 |
| Price | 25% | 41 | 58 | 44 |
| Inputs & features | 15% | 35 | 70 | 90 |
| Context window | 10% | 32 | 61 | 37 |
| Overall | 100% | 55/100 | 70/100 | 65/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5Z.ai (Zhipu) | Inkling SmallThinking Machines | Qwen3.6 27BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 150.2 (best) | 146.5 |
| ECI rank | #74 of 148 | #47 of 148 (best) | #68 of 148 |
| GPQA DiamondGraduate-level science questions | 87.8% | 88.5% (best) | 85.9% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 46.3% (best) | 35.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | 90.0% | 91.1% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | — | — |
| SimpleQA VerifiedShort factual questions | — | 19.1% | — |
| Price per million tokens | |||
| Input | $1.00 | $0.50 (best) | $0.60 |
| Output | $3.20 | $1.20 (best) | $3.60 |
| Cached input | $0.20 | — | — |
| Blended (3:1) | $1.55 | $0.675 (best) | $1.35 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Median of 11 providers | Official Alibaba API |
| Limits | |||
| Context window | 204,800 tokens | 1,048,576 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens | 1,048,576 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | Yes | Yes |
| Video | No | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | OpenApache-2.0 | Open |
| API model ID | glm-5 | — | qwen3.6-27b |
| API providers | 27 (best) | 11 | 27 (best) |
| Released | Feb 12, 2026 | Jul 30, 2026 | Apr 22, 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$16.40
- Inkling Small$7.40
- Qwen3.6 27B$13.20
Which should you choose?
Which is better: GLM-5, Inkling Small or Qwen3.6 27B?
Inkling Small is the better all-round choice, scoring 70/100 against Qwen3.6 27B (65) and GLM-5 (55). It leads on capability, price and context window. Qwen3.6 27B wins on inputs & features. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5, Inkling Small or Qwen3.6 27B?
Inkling Small is cheaper at $0.50 input / $1.20 output per million tokens (median across 11 API providers). Qwen3.6 27B costs $0.60 input / $3.60 output per million tokens (official Alibaba API price); GLM-5 costs $1.00 input / $3.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.675 per million tokens for Inkling Small versus $1.35 for Qwen3.6 27B (2× as much) and $1.55 for GLM-5 (2.3× as much).
Which scores higher on benchmarks?
Inkling Small scores higher on the Capabilities Index (ECI): Inkling Small 150.2 (#47 of 148), Qwen3.6 27B 146.5 (#68 of 148) and GLM-5 145.8 (#74 of 148). The confidence ranges of the top two overlap (147.5–152.1 vs 144.2–147.9), so treat the gap as small. On individual benchmarks: GPQA Diamond — Inkling Small 88.5%, GLM-5 87.8%, Qwen3.6 27B 85.9%; OTIS Mock AIME 2024–2025 — Qwen3.6 27B 91.1%, Inkling Small 90.0%, GLM-5 80.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Inkling Small and Qwen3.6 27B yet, so there is no like-for-like coding score. On overall capability, Inkling Small 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 262,144 for Qwen3.6 27B and 204,800 for GLM-5. Maximum output per response: GLM-5 up to 131,072, Inkling Small up to 1,048,576, Qwen3.6 27B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; Inkling Small accepts text, images and audio; Qwen3.6 27B accepts text, images, audio and video. Qwen3.6 27B 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?
Inkling Small is the newest, released Jul 30, 2026. Qwen3.6 27B came out Apr 22, 2026; GLM-5 came out Feb 12, 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.
