GLM-4.7 vs Claude Haiku 4.5 vs Llama 3.3 Nemotron Super 49B v1.5
Too close to call on our weighted score (Llama 3.3 Nemotron Super 49B v1.5 48, Claude Haiku 4.5 47, GLM-4.7 41). The right pick depends on what you value most.
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
NVIDIALlama 3.3 Nemotron Super 49B v1.5
48/100- ECI—
- Price$0.40 / $0.40
- Context131K
Too close to call
It is close. Our weighted score puts them within a point (Llama 3.3 Nemotron Super 49B v1.5 48/100, Claude Haiku 4.5 47/100, GLM-4.7 41/100), so choose by what matters most for your work: Llama 3.3 Nemotron Super 49B v1.5 on price and GLM-4.7 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 priceLlama 3.3 Nemotron Super 49B v1.5Llama 3.3 Nemotron Super 49B v1.5 $0.40 · 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 · Llama 3.3 Nemotron Super 49B v1.5 131,072 tokens
- Widest inputsClaude Haiku 4.5GLM-4.7: Text · Claude Haiku 4.5: Text, Images, PDFs · Llama 3.3 Nemotron Super 49B v1.5: Text
- Self-hostingGLM-4.7 and Llama 3.3 Nemotron Super 49B v1.5Publishes downloadable weights
| Measure | Weight | GLM-4.7 | Claude Haiku 4.5 | Llama 3.3 Nemotron Super 49B v1.5 |
|---|---|---|---|---|
| Price | 50% | 48 | 34 | 66 |
| Inputs & features | 30% | 35 | 80 | 35 |
| Context window | 20% | 32 | 32 | 25 |
| Overall | 100% | 41/100 | 47/100 | 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 | GLM-4.7Z.ai (Zhipu) | Claude Haiku 4.5Anthropic | Llama 3.3 Nemotron Super 49B v1.5NVIDIA |
|---|---|---|---|
| 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 | $0.60 | $1.00 | $0.40 (best) |
| Output | $2.20 | $5.00 | $0.40 (best) |
| Cached input | $0.11 | $0.10 (best) | — |
| Blended (3:1) | $1.00 | $2.00 | $0.40 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Anthropic API | Median of 1 providers |
| Limits | |||
| Context window | 204,800 tokens (best) | 200,000 tokens | 131,072 tokens |
| Max output | 131,072 tokens (best) | 64,000 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | Yes | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | glm-4.7 | claude-haiku-4-5 | — |
| API providers | 31 | 48 (best) | 1 |
| Released | Dec 22, 2025 | Oct 15, 2025 | Jul 25, 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-4.7$10.40
- Claude Haiku 4.5$20.00
- Llama 3.3 Nemotron Super 49B v1.5$4.80
Which should you choose?
Which is better: GLM-4.7, Claude Haiku 4.5 or Llama 3.3 Nemotron Super 49B v1.5?
It is close. Our weighted score puts them within a point (Llama 3.3 Nemotron Super 49B v1.5 48/100, Claude Haiku 4.5 47/100, GLM-4.7 41/100), so choose by what matters most for your work: Llama 3.3 Nemotron Super 49B v1.5 on price and GLM-4.7 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, GLM-4.7, Claude Haiku 4.5 or Llama 3.3 Nemotron Super 49B v1.5?
Llama 3.3 Nemotron Super 49B v1.5 is cheaper at $0.40 input / $0.40 output per million tokens (median across 1 API provider). 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 $0.40 per million tokens for Llama 3.3 Nemotron Super 49B v1.5 versus $1.00 for GLM-4.7 (2.5× as much) and $2.00 for Claude Haiku 4.5 (5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-4.7 has an ECI of 143.7, Claude Haiku 4.5 has an ECI of 142.2 and Llama 3.3 Nemotron Super 49B v1.5 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7, Claude Haiku 4.5 and Llama 3.3 Nemotron Super 49B v1.5 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 131,072 for Llama 3.3 Nemotron Super 49B v1.5. Maximum output per response: GLM-4.7 up to 131,072, Claude Haiku 4.5 up to 64,000, Llama 3.3 Nemotron Super 49B v1.5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; Claude Haiku 4.5 accepts text, images and PDFs; Llama 3.3 Nemotron Super 49B v1.5 accepts text. Claude Haiku 4.5 handles the widest range of inputs.
Are any of these open source?
GLM-4.7 and Llama 3.3 Nemotron Super 49B v1.5 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. Claude Haiku 4.5 came out Oct 15, 2025; Llama 3.3 Nemotron Super 49B v1.5 came out Jul 25, 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.
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