GLM-5 vs Grok 4.3 vs Qwen3.6 27B
Too close to call on our weighted score (Grok 4.3 67, 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
xAIGrok 4.3
67/100- ECI149.2
- Price$1.25 / $2.50
- Context1M
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 2 points (Grok 4.3 67/100, Qwen3.6 27B 65/100, GLM-5 55/100), so choose by what matters most for your work: Grok 4.3 for raw capability and Qwen3.6 27B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGrok 4.3Capabilities Index (ECI): Grok 4.3 149.2 · Qwen3.6 27B 146.5 · GLM-5 145.8
- Lowest priceQwen3.6 27BQwen3.6 27B $1.35 · GLM-5 $1.55 · Grok 4.3 $1.56 per 1M tokens (3:1 blend)
- Longest contextGrok 4.3Grok 4.3 1,000,000 · Qwen3.6 27B 262,144 · GLM-5 204,800 tokens
- Widest inputsQwen3.6 27BGLM-5: Text · Grok 4.3: Text, Images, PDFs · Qwen3.6 27B: Text, Images, Audio, Video
- Self-hostingGLM-5 and Qwen3.6 27BPublishes downloadable weights
| Measure | Weight | GLM-5 | Grok 4.3 | Qwen3.6 27B |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 73 | 77 | 74 |
| Price | 25% | 41 | 41 | 44 |
| Inputs & features | 15% | 35 | 80 | 90 |
| Context window | 10% | 32 | 60 | 37 |
| Overall | 100% | 55/100 | 67/100 | 65/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5Z.ai (Zhipu) | Grok 4.3xAI | Qwen3.6 27BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | 149.2 (best) | 146.5 |
| ECI rank | #74 of 148 | #55 of 148 (best) | #68 of 148 |
| GPQA DiamondGraduate-level science questions | 87.8% | 88.8% (best) | 85.9% |
| FrontierMath Tiers 1–3Research-level mathematics | — | 42.8% (best) | 35.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | 93.3% (best) | 91.1% |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | — | — |
| SimpleQA VerifiedShort factual questions | — | 33.2% | — |
| Price per million tokens | |||
| Input | $1.00 | $1.25 | $0.60 (best) |
| Output | $3.20 | $2.50 (best) | $3.60 |
| Cached input | $0.20 | $0.20 | — |
| Blended (3:1) | $1.55 | $1.56 | $1.35 (best) |
| Long-context rate | Same rate | Over 200K: $2.50 / $5.00 | Same rate |
| Price source | Official Z.AI API | Official xAI API | Official Alibaba API |
| Limits | |||
| Context window | 204,800 tokens | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens (best) | 30,000 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | No |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yes | Yeslow · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-5 | grok-4.3 | qwen3.6-27b |
| API providers | 27 | 27 | 27 |
| Released | Feb 12, 2026 | Apr 17, 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
- Grok 4.3$17.50
- Qwen3.6 27B$13.20
Which should you choose?
Which is better: GLM-5, Grok 4.3 or Qwen3.6 27B?
It is close. Our weighted score puts them within 2 points (Grok 4.3 67/100, Qwen3.6 27B 65/100, GLM-5 55/100), so choose by what matters most for your work: Grok 4.3 for raw capability and Qwen3.6 27B on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GLM-5, Grok 4.3 or Qwen3.6 27B?
Qwen3.6 27B is cheaper at $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); Grok 4.3 costs $1.25 input / $2.50 output per million tokens (official xAI API price). At a typical mix of three input tokens to one output token, that is $1.35 per million tokens for Qwen3.6 27B versus $1.55 for GLM-5 (1.1× as much) and $1.56 for Grok 4.3 (1.2× as much).
Which scores higher on benchmarks?
Grok 4.3 scores higher on the Capabilities Index (ECI): Grok 4.3 149.2 (#55 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 (147.6–150.8 vs 144.2–147.9), so treat the gap as small. On individual benchmarks: GPQA Diamond — Grok 4.3 88.8%, GLM-5 87.8%, Qwen3.6 27B 85.9%; OTIS Mock AIME 2024–2025 — Grok 4.3 93.3%, Qwen3.6 27B 91.1%, GLM-5 80.0%.
Which is better for coding?
There are no published SWE-bench Verified results for Grok 4.3 and Qwen3.6 27B yet, so there is no like-for-like coding score. On overall capability, Grok 4.3 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?
Grok 4.3 has the largest context window at 1,000,000 tokens, against 262,144 for Qwen3.6 27B and 204,800 for GLM-5. Maximum output per response: GLM-5 up to 131,072, Grok 4.3 up to 30,000, Qwen3.6 27B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; Grok 4.3 accepts text, images and PDFs; 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; Grok 4.3 is proprietary.
Which is newer?
Qwen3.6 27B is the newest, released Apr 22, 2026. Grok 4.3 came out Apr 17, 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.
