GLM-4.6V-Flash vs GLM-4.7 vs Claude Haiku 4.5
GLM-4.6V-Flash comes out ahead, 63 to 47 and 41 on our weighted score, and it is the cheaper option too.
- Our pick
Z.ai (Zhipu)GLM-4.6V-Flash
63/100- ECI—
- Price$0.161 / $0.559
- Context128K
- Z.ai (Zhipu)
GLM-4.7
41/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
AnthropicClaude Haiku 4.5
47/100- ECI142.2
- Price$1.00 / $5.00
- Context200K
GLM-4.6V-Flash is our pick
GLM-4.6V-Flash is the better all-round choice, scoring 63/100 against Claude Haiku 4.5 (47) and GLM-4.7 (41). It leads on price. Claude Haiku 4.5 wins 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 priceGLM-4.6V-FlashGLM-4.6V-Flash $0.261 · GLM-4.7 $1.00 · Claude Haiku 4.5 $2.00 per 1M tokens (3:1 blend)
- Longest contextGLM-4.7GLM-4.7 204,800 · Claude Haiku 4.5 200,000 · GLM-4.6V-Flash 128,000 tokens
- Widest inputsGLM-4.6V-Flash and Claude Haiku 4.5GLM-4.6V-Flash: Text, Images, Video · GLM-4.7: Text · Claude Haiku 4.5: Text, Images, PDFs
- Self-hostingGLM-4.6V-Flash and GLM-4.7Publishes downloadable weights
| Measure | Weight | GLM-4.6V-Flash | GLM-4.7 | Claude Haiku 4.5 |
|---|---|---|---|---|
| Price | 50% | 74 | 48 | 34 |
| Inputs & features | 30% | 70 | 35 | 80 |
| Context window | 20% | 24 | 32 | 32 |
| Overall | 100% | 63/100 | 41/100 | 47/100 |
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-4.6V-FlashZ.ai (Zhipu) | GLM-4.7Z.ai (Zhipu) | Claude Haiku 4.5Anthropic |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 143.7 (best) | 142.2 |
| ECI rank | — | #94 of 165 (best) | #102 of 165 |
| GPQA DiamondGraduate-level science questions | — | 83.3% (best) | 71.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 83.3% (best) | 66.7% |
| SimpleQA VerifiedShort factual questions | — | 32.2% (best) | 13.2% |
| Price per million tokens | |||
| Input | $0.161 (best) | $0.60 | $1.00 |
| Output | $0.559 (best) | $2.20 | $5.00 |
| Cached input | — | $0.11 | $0.10 (best) |
| Blended (3:1) | $0.261 (best) | $1.00 | $2.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Z.AI API | Official Anthropic API |
| Limits | |||
| Context window | 128,000 tokens | 204,800 tokens (best) | 200,000 tokens |
| Max output | 32,768 tokens | 131,072 tokens (best) | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | glm-4.6v-flash | glm-4.7 | claude-haiku-4-5 |
| API providers | 6 | 31 | 48 (best) |
| Released | Dec 8, 2025 | Dec 22, 2025 | Oct 15, 2025 |
| Knowledge cutoff | — | 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.
- GLM-4.6V-Flash$2.73
- GLM-4.7$10.40
- Claude Haiku 4.5$20.00
Which should you choose?
Which is better: GLM-4.6V-Flash, GLM-4.7 or Claude Haiku 4.5?
GLM-4.6V-Flash is the better all-round choice, scoring 63/100 against Claude Haiku 4.5 (47) and GLM-4.7 (41). It leads on price. Claude Haiku 4.5 wins 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-4.6V-Flash, GLM-4.7 or Claude Haiku 4.5?
GLM-4.6V-Flash is cheaper at $0.161 input / $0.559 output per million tokens (median across 2 API providers; free on Z.AI). GLM-4.7 costs $0.60 input / $2.20 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). At a typical mix of three input tokens to one output token, that is $0.261 per million tokens for GLM-4.6V-Flash versus $1.00 for GLM-4.7 (3.8× as much) and $2.00 for Claude Haiku 4.5 (7.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.6V-Flash has not been scored yet, GLM-4.7 has an ECI of 143.7 and Claude Haiku 4.5 has an ECI of 142.2.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.6V-Flash, GLM-4.7 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?
GLM-4.7 has the largest context window at 204,800 tokens, against 200,000 for Claude Haiku 4.5 and 128,000 for GLM-4.6V-Flash. Maximum output per response: GLM-4.6V-Flash up to 32,768, GLM-4.7 up to 131,072, Claude Haiku 4.5 up to 64,000 tokens.
Which can read images, PDFs, audio or video?
GLM-4.6V-Flash accepts text, images and video; GLM-4.7 accepts text; Claude Haiku 4.5 accepts text, images and PDFs. GLM-4.6V-Flash handles the widest range of inputs.
Are any of these open source?
GLM-4.6V-Flash and GLM-4.7 publishes its weights and can be self-hosted; Claude Haiku 4.5 is proprietary.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. GLM-4.6V-Flash came out Dec 8, 2025; Claude Haiku 4.5 came out Oct 15, 2025. Knowledge cutoff: GLM-4.7 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 Nov 2, 2026.
