Qwen3 235B-A22B Thinking 2507 vs Llama 3.3 Nemotron Super 49B v1.5 vs GLM-4.7
Llama 3.3 Nemotron Super 49B v1.5 comes out ahead, 48 to 42 and 41 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
- Our pick
NVIDIALlama 3.3 Nemotron Super 49B v1.5
48/100- ECI—
- Price$0.40 / $0.40
- Context131K
Z.ai (Zhipu)GLM-4.7
41/100- ECI143.7
- Price$0.60 / $2.20
- Context205K
Llama 3.3 Nemotron Super 49B v1.5 is our pick
Llama 3.3 Nemotron Super 49B v1.5 is the better all-round choice, scoring 48/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on price. Qwen3 235B-A22B Thinking 2507 wins on context window. 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 · Qwen3 235B-A22B Thinking 2507 $0.95 · GLM-4.7 $1.00 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · GLM-4.7 204,800 · Llama 3.3 Nemotron Super 49B v1.5 131,072 tokens
- Widest inputsSame inputsQwen3 235B-A22B Thinking 2507: Text · Llama 3.3 Nemotron Super 49B v1.5: Text · GLM-4.7: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3 235B-A22B Thinking 2507 | Llama 3.3 Nemotron Super 49B v1.5 | GLM-4.7 |
|---|---|---|---|---|
| Price | 50% | 49 | 66 | 48 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 37 | 25 | 32 |
| Overall | 100% | 42/100 | 48/100 | 41/100 |
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 235B-A22B Thinking 2507Alibaba (Qwen) | Llama 3.3 Nemotron Super 49B v1.5NVIDIA | GLM-4.7Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.8 (best) | — | 143.7 |
| ECI rank | #93 of 165 (best) | — | #94 of 165 |
| GPQA DiamondGraduate-level science questions | 80.1% | — | 83.3% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 86.7% (best) | — | 83.3% |
| SimpleQA VerifiedShort factual questions | 40.4% (best) | — | 32.2% |
| Price per million tokens | |||
| Input | $0.30 (best) | $0.40 | $0.60 |
| Output | $2.90 | $0.40 (best) | $2.20 |
| Cached input | — | — | $0.11 |
| Blended (3:1) | $0.95 | $0.40 (best) | $1.00 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 11 providers | Median of 1 providers | Official Z.AI API |
| Limits | |||
| Context window | 262,144 tokens (best) | 131,072 tokens | 204,800 tokens |
| Max output | 81,920 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache 2.0 | Open | Open |
| API model ID | — | — | glm-4.7 |
| API providers | 12 | 1 | 31 (best) |
| Released | Jul 25, 2025 | Jul 25, 2025 | Dec 22, 2025 |
| Knowledge cutoff | — | — | Apr 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 235B-A22B Thinking 2507$8.80
- Llama 3.3 Nemotron Super 49B v1.5$4.80
- GLM-4.7$10.40
Which should you choose?
Which is better: Qwen3 235B-A22B Thinking 2507, Llama 3.3 Nemotron Super 49B v1.5 or GLM-4.7?
Llama 3.3 Nemotron Super 49B v1.5 is the better all-round choice, scoring 48/100 against Qwen3 235B-A22B Thinking 2507 (42) and GLM-4.7 (41). It leads on price. Qwen3 235B-A22B Thinking 2507 wins on context window. 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 235B-A22B Thinking 2507, Llama 3.3 Nemotron Super 49B v1.5 or GLM-4.7?
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). Qwen3 235B-A22B Thinking 2507 costs $0.30 input / $2.90 output per million tokens (median across 11 API providers); GLM-4.7 costs $0.60 input / $2.20 output per million tokens (official Z.AI 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 $0.95 for Qwen3 235B-A22B Thinking 2507 (2.4× as much) and $1.00 for GLM-4.7 (2.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8, Llama 3.3 Nemotron Super 49B v1.5 has not been scored yet and GLM-4.7 has an ECI of 143.7.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 235B-A22B Thinking 2507, Llama 3.3 Nemotron Super 49B v1.5 and GLM-4.7 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 235B-A22B Thinking 2507 has the largest context window at 262,144 tokens, against 204,800 for GLM-4.7 and 131,072 for Llama 3.3 Nemotron Super 49B v1.5. Maximum output per response: Qwen3 235B-A22B Thinking 2507 up to 81,920, Llama 3.3 Nemotron Super 49B v1.5 up to 131,072, GLM-4.7 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3 235B-A22B Thinking 2507 accepts text; Llama 3.3 Nemotron Super 49B v1.5 accepts text; GLM-4.7 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
GLM-4.7 is the newest, released Dec 22, 2025. Qwen3 235B-A22B Thinking 2507 came out Jul 25, 2025; Llama 3.3 Nemotron Super 49B v1.5 came out Jul 25, 2025. Knowledge cutoff: GLM-4.7 Apr 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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