GLM-5-Turbo vs Grok 4.20 (Non-Reasoning) vs Grok 4.3
Grok 4.3 comes out ahead, 56 to 53 and 38 on our weighted score.
Z.ai (Zhipu)GLM-5-Turbo
38/100- ECI—
- Price$1.20 / $4.00
- Context200K
xAIGrok 4.20 (Non-Reasoning)
53/100- ECI—
- Price$1.25 / $2.50
- Context1M
- Our pickxAI
Grok 4.3
56/100- ECI149.2
- Price$1.25 / $2.50
- Context1M
Grok 4.3 is our pick
Grok 4.3 is the better all-round choice, scoring 56/100 against Grok 4.20 (Non-Reasoning) (53) and GLM-5-Turbo (38). It leads on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGrok 4.20 (Non-Reasoning) and Grok 4.3Grok 4.20 (Non-Reasoning) $1.56 · Grok 4.3 $1.56 · GLM-5-Turbo $1.90 per 1M tokens (3:1 blend)
- Longest contextGrok 4.20 (Non-Reasoning) and Grok 4.3Grok 4.20 (Non-Reasoning) 1,000,000 · Grok 4.3 1,000,000 · GLM-5-Turbo 200,000 tokens
- Widest inputsGrok 4.20 (Non-Reasoning) and Grok 4.3GLM-5-Turbo: Text · Grok 4.20 (Non-Reasoning): Text, Images, PDFs · Grok 4.3: Text, Images, PDFs
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | GLM-5-Turbo | Grok 4.20 (Non-Reasoning) | Grok 4.3 |
|---|---|---|---|---|
| Price | 50% | 37 | 41 | 41 |
| Inputs & features | 30% | 45 | 70 | 80 |
| Context window | 20% | 32 | 60 | 60 |
| Overall | 100% | 38/100 | 53/100 | 56/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GLM-5-TurboZ.ai (Zhipu) | Grok 4.20 (Non-Reasoning)xAI | Grok 4.3xAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 149.2 |
| ECI rank | — | — | #55 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 88.8% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 42.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 93.3% |
| SimpleQA VerifiedShort factual questions | — | — | 33.2% |
| Price per million tokens | |||
| Input | $1.20 (best) | $1.25 | $1.25 |
| Output | $4.00 | $2.50 (best) | $2.50 (best) |
| Cached input | $0.24 | $0.20 (best) | $0.20 (best) |
| Blended (3:1) | $1.90 | $1.56 (best) | $1.56 (best) |
| Long-context rate | Same rate | Over 200K: $2.50 / $5.00 | Over 200K: $2.50 / $5.00 |
| Price source | Official Z.AI API | Official xAI API | Official xAI API |
| Limits | |||
| Context window | 200,000 tokens | 1,000,000 tokens (best) | 1,000,000 tokens (best) |
| Max output | 131,072 tokens (best) | 30,000 tokens | 30,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | Yes | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yeslow · medium · high |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | glm-5-turbo | grok-4.20-0309-non-reasoning | grok-4.3 |
| API providers | 17 | 10 | 27 (best) |
| Released | Mar 16, 2026 | Mar 9, 2026 | Apr 17, 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-Turbo$20.00
- Grok 4.20 (Non-Reasoning)$17.50
- Grok 4.3$17.50
Which should you choose?
Which is better: GLM-5-Turbo, Grok 4.20 (Non-Reasoning) or Grok 4.3?
Grok 4.3 is the better all-round choice, scoring 56/100 against Grok 4.20 (Non-Reasoning) (53) and GLM-5-Turbo (38). It leads on inputs & features. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GLM-5-Turbo, Grok 4.20 (Non-Reasoning) or Grok 4.3?
Grok 4.20 (Non-Reasoning) is cheaper at $1.25 input / $2.50 output per million tokens (official xAI API price). Grok 4.3 costs $1.25 input / $2.50 output per million tokens (official xAI API price); GLM-5-Turbo costs $1.20 input / $4.00 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $1.56 per million tokens for Grok 4.20 (Non-Reasoning) versus $1.56 for Grok 4.3 (1× as much) and $1.90 for GLM-5-Turbo (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-5-Turbo has not been scored yet, Grok 4.20 (Non-Reasoning) has not been scored yet and Grok 4.3 has an ECI of 149.2.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-5-Turbo, Grok 4.20 (Non-Reasoning) and Grok 4.3 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Grok 4.20 (Non-Reasoning) and Grok 4.3 have the largest context windows (1,000,000 and 1,000,000 tokens), against 200,000 for GLM-5-Turbo. Maximum output per response: GLM-5-Turbo up to 131,072, Grok 4.20 (Non-Reasoning) up to 30,000, Grok 4.3 up to 30,000 tokens.
Which can read images, PDFs, audio or video?
GLM-5-Turbo accepts text; Grok 4.20 (Non-Reasoning) accepts text, images and PDFs; Grok 4.3 accepts text, images and PDFs. Grok 4.20 (Non-Reasoning) handles the widest range of inputs.
Are any of these open source?
No. GLM-5-Turbo, Grok 4.20 (Non-Reasoning) and Grok 4.3 are proprietary and only available through APIs and apps.
Which is newer?
Grok 4.3 is the newest, released Apr 17, 2026. GLM-5-Turbo came out Mar 16, 2026; Grok 4.20 (Non-Reasoning) came out Mar 9, 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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