Qwen3 Max vs GLM-4.5V vs Claude Haiku 4.5
Too close to call on our weighted score (GLM-4.5V 49, Claude Haiku 4.5 48, Qwen3 Max 31). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3 Max
31/100- ECI142.4
- Price$1.20 / $6.00
- Context262K
Z.ai (Zhipu)GLM-4.5V
49/100- ECI—
- Price$0.60 / $1.80
- Context64K
AnthropicClaude Haiku 4.5
48/100- ECI142.4
- Price$1.00 / $5.00
- Context200K
Too close to call
It is close. Our weighted score puts them within 1 points (GLM-4.5V 49/100, Claude Haiku 4.5 48/100, Qwen3 Max 31/100), so choose by what matters most for your work: GLM-4.5V on price and Qwen3 Max for long inputs. 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 priceGLM-4.5VGLM-4.5V $0.90 · Claude Haiku 4.5 $2.00 · Qwen3 Max $2.40 per 1M tokens (3:1 blend)
- Longest contextQwen3 MaxQwen3 Max 262,144 · Claude Haiku 4.5 200,000 · GLM-4.5V 64,000 tokens
- Widest inputsGLM-4.5V and Claude Haiku 4.5Qwen3 Max: Text · GLM-4.5V: Text, Images, Video · Claude Haiku 4.5: Text, Images, PDFs
- Self-hostingGLM-4.5VPublishes downloadable weights
| Measure | Weight | Qwen3 Max | GLM-4.5V | Claude Haiku 4.5 |
|---|---|---|---|---|
| Price | 50% | 32 | 52 | 36 |
| Inputs & features | 30% | 25 | 70 | 80 |
| Context window | 20% | 37 | 12 | 32 |
| Overall | 100% | 31/100 | 49/100 | 48/100 |
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 | Qwen3 MaxAlibaba (Qwen) | GLM-4.5VZ.ai (Zhipu) | Claude Haiku 4.5Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 142.4 | — | 142.4 (best) |
| ECI rank | #91 of 148 | — | #90 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 72.6% (best) | — | 71.2% |
| FrontierMath Tiers 1–3Research-level mathematics | 19.0% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 73.3% (best) | — | 66.7% |
| SimpleQA VerifiedShort factual questions | 48.8% (best) | — | 13.2% |
| Price per million tokens | |||
| Input | $1.20 | $0.60 (best) | $1.00 |
| Output | $6.00 | $1.80 (best) | $5.00 |
| Cached input | — | — | $0.10 |
| Blended (3:1) | $2.40 | $0.90 (best) | $2.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official Z.AI API | Official Anthropic API |
| Limits | |||
| Context window | 262,144 tokens (best) | 64,000 tokens | 200,000 tokens |
| Max output | 65,536 tokens (best) | 16,384 tokens | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | qwen3-max | glm-4.5v | claude-haiku-4-5 |
| API providers | 16 | 11 | 34 (best) |
| Released | Sep 23, 2025 | Aug 11, 2025 | Oct 15, 2025 |
| Knowledge cutoff | Apr 2025 | Apr 2025 | Feb 28, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3 Max$24.00
- GLM-4.5V$9.60
- Claude Haiku 4.5$20.00
Which should you choose?
Which is better: Qwen3 Max, GLM-4.5V or Claude Haiku 4.5?
It is close. Our weighted score puts them within 1 points (GLM-4.5V 49/100, Claude Haiku 4.5 48/100, Qwen3 Max 31/100), so choose by what matters most for your work: GLM-4.5V on price and Qwen3 Max for long inputs. 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, Qwen3 Max, GLM-4.5V or Claude Haiku 4.5?
GLM-4.5V is cheaper at $0.60 input / $1.80 output per million tokens (official Z.AI API price). Claude Haiku 4.5 costs $1.00 input / $5.00 output per million tokens (official Anthropic API price); Qwen3 Max costs $1.20 input / $6.00 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.90 per million tokens for GLM-4.5V versus $2.00 for Claude Haiku 4.5 (2.2× as much) and $2.40 for Qwen3 Max (2.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3 Max has an ECI of 142.4, GLM-4.5V has not been scored yet and Claude Haiku 4.5 has an ECI of 142.4.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 Max, GLM-4.5V and Claude Haiku 4.5 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?
Qwen3 Max has the largest context window at 262,144 tokens, against 200,000 for Claude Haiku 4.5 and 64,000 for GLM-4.5V. Maximum output per response: Qwen3 Max up to 65,536, GLM-4.5V up to 16,384, Claude Haiku 4.5 up to 64,000 tokens.
Which can read images, PDFs, audio or video?
Qwen3 Max accepts text; GLM-4.5V accepts text, images and video; Claude Haiku 4.5 accepts text, images and PDFs. GLM-4.5V handles the widest range of inputs.
Are any of these open source?
GLM-4.5V publishes its weights and can be self-hosted; Qwen3 Max and Claude Haiku 4.5 is proprietary.
Which is newer?
Claude Haiku 4.5 is the newest, released Oct 15, 2025. Qwen3 Max came out Sep 23, 2025; GLM-4.5V came out Aug 11, 2025. Knowledge cutoff: Qwen3 Max Apr 2025, GLM-4.5V Apr 2025, Claude Haiku 4.5 Feb 28, 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.
