GLM-5 vs GPT-5.4 nano vs Qwen3.6 27B
Too close to call on our weighted score (GPT-5.4 nano 68, Qwen3.6 27B 65, GLM-5 55). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-5
55/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
OpenAIGPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
Alibaba (Qwen)Qwen3.6 27B
65/100- ECI146.5
- Price$0.60 / $3.60
- Context262K
Too close to call
It is close. Our weighted score puts them within 3 points (GPT-5.4 nano 68/100, Qwen3.6 27B 65/100, GLM-5 55/100), so choose by what matters most for your work: Qwen3.6 27B for raw capability, GPT-5.4 nano on price and GPT-5.4 nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.6 27BCapabilities Index (ECI): Qwen3.6 27B 146.5 · GLM-5 145.8 · GPT-5.4 nano 145.8
- Lowest priceGPT-5.4 nanoGPT-5.4 nano $0.463 · Qwen3.6 27B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · Qwen3.6 27B 262,144 · GLM-5 204,800 tokens
- Widest inputsQwen3.6 27BGLM-5: Text · GPT-5.4 nano: Text, Images · Qwen3.6 27B: Text, Images, Audio, Video
- Self-hostingGLM-5 and Qwen3.6 27BPublishes downloadable weights
| Measure | Weight | GLM-5 | GPT-5.4 nano | Qwen3.6 27B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 73 | 74 |
| Price | 25% | 41 | 66 | 44 |
| Inputs & features | 15% | 35 | 70 | 90 |
| Context window | 10% | 32 | 44 | 37 |
| Overall | 100% | 55/100 | 68/100 | 65/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5Z.ai (Zhipu) | GPT-5.4 nanoOpenAI | Qwen3.6 27BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 145.8 | 146.5 (best) |
| ECI rank | #74 of 148 | #75 of 148 | #68 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 87.8% (best) | 78.5% | 85.9% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 44.9% (best) | 35.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | 87.8% | 91.1% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | — | — |
| SimpleQA VerifiedShort factual questions | — | 11.7% | — |
| Price per million tokens | |||
| Input | $1.00 | $0.20 (best) | $0.60 |
| Output | $3.20 | $1.25 (best) | $3.60 |
| Cached input | $0.20 | $0.02 (best) | — |
| Blended (3:1) | $1.55 | $0.463 (best) | $1.35 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official OpenAI API | Official Alibaba API |
| Limits | |||
| Context window | 204,800 tokens | 400,000 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens (best) | 128,000 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yes | Yeslow · medium · high · xhigh | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-5 | gpt-5.4-nano | qwen3.6-27b |
| API providers | 27 (best) | 26 | 27 (best) |
| Released | Feb 12, 2026 | Mar 17, 2026 | Apr 22, 2026 |
| Knowledge cutoff | — | Aug 31, 2025 | — |
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
- GPT-5.4 nano$4.50
- Qwen3.6 27B$13.20
Which should you choose?
Which is better: GLM-5, GPT-5.4 nano or Qwen3.6 27B?
It is close. Our weighted score puts them within 3 points (GPT-5.4 nano 68/100, Qwen3.6 27B 65/100, GLM-5 55/100), so choose by what matters most for your work: Qwen3.6 27B for raw capability, GPT-5.4 nano on price and GPT-5.4 nano for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5, GPT-5.4 nano or Qwen3.6 27B?
GPT-5.4 nano is cheaper at $0.20 input / $1.25 output per million tokens (official OpenAI API price). 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 $0.463 per million tokens for GPT-5.4 nano versus $1.35 for Qwen3.6 27B (2.9× as much) and $1.55 for GLM-5 (3.4× as much).
Which scores higher on benchmarks?
Qwen3.6 27B scores higher on the Capabilities Index (ECI): Qwen3.6 27B 146.5 (#68 of 148), GLM-5 145.8 (#74 of 148) and GPT-5.4 nano 145.8 (#75 of 148). The confidence ranges of the top two overlap (144.2–147.9 vs 143.9–147.7), so treat the gap as small. On individual benchmarks: GPQA Diamond — GLM-5 87.8%, Qwen3.6 27B 85.9%, GPT-5.4 nano 78.5%; OTIS Mock AIME 2024–2025 — Qwen3.6 27B 91.1%, GPT-5.4 nano 87.8%, GLM-5 80.0%.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.4 nano and Qwen3.6 27B yet, so there is no like-for-like coding score. On overall capability, Qwen3.6 27B 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?
GPT-5.4 nano has the largest context window at 400,000 tokens, against 262,144 for Qwen3.6 27B and 204,800 for GLM-5. Maximum output per response: GLM-5 up to 131,072, GPT-5.4 nano up to 128,000, Qwen3.6 27B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; GPT-5.4 nano accepts text and images; Qwen3.6 27B accepts text, images, audio and video. Qwen3.6 27B handles the widest range of inputs.
Are any of these open source?
GLM-5 and Qwen3.6 27B publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.
Which is newer?
Qwen3.6 27B is the newest, released Apr 22, 2026. GPT-5.4 nano came out Mar 17, 2026; GLM-5 came out Feb 12, 2026. Knowledge cutoff: GPT-5.4 nano Aug 31, 2025.
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.
