GLM-4.7 vs Jamba Large vs Qwen3 235B-A22B Thinking 2507
Too close to call on our weighted score (Qwen3 235B-A22B Thinking 2507 42, GLM-4.7 41, Jamba Large 29). 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
AI21 LabsJamba Large
29/100- ECI—
- Price$2.00 / $8.00
- Context256K
Alibaba (Qwen)Qwen3 235B-A22B Thinking 2507
42/100- ECI143.8
- Price$0.30 / $2.90
- Context262K
Too close to call
It is close. Our weighted score puts them within 1 points (Qwen3 235B-A22B Thinking 2507 42/100, GLM-4.7 41/100, Jamba Large 29/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 on price and Qwen3 235B-A22B Thinking 2507 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 priceQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 $0.95 · GLM-4.7 $1.00 · Jamba Large $3.50 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B Thinking 2507Qwen3 235B-A22B Thinking 2507 262,144 · Jamba Large 256,000 · GLM-4.7 204,800 tokens
- Widest inputsSame inputsGLM-4.7: Text · Jamba Large: Text · Qwen3 235B-A22B Thinking 2507: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-4.7 | Jamba Large | Qwen3 235B-A22B Thinking 2507 |
|---|---|---|---|---|
| Price | 50% | 48 | 23 | 49 |
| Inputs & features | 30% | 35 | 35 | 35 |
| Context window | 20% | 32 | 36 | 37 |
| Overall | 100% | 41/100 | 29/100 | 42/100 |
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) | Jamba LargeAI21 Labs | Qwen3 235B-A22B Thinking 2507Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 143.7 | — | 143.8 (best) |
| ECI rank | #94 of 165 | — | #93 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 83.3% (best) | — | 80.1% |
| OTIS Mock AIME 2024–2025Competition mathematics | 83.3% | — | 86.7% (best) |
| SimpleQA VerifiedShort factual questions | 32.2% | — | 40.4% (best) |
| Price per million tokens | |||
| Input | $0.60 | $2.00 | $0.30 (best) |
| Output | $2.20 (best) | $8.00 | $2.90 |
| Cached input | $0.11 | — | — |
| Blended (3:1) | $1.00 | $3.50 | $0.95 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official AI21 Labs API | Median of 11 providers |
| Limits | |||
| Context window | 204,800 tokens | 256,000 tokens | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 4,096 tokens | 81,920 tokens |
| 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 | No | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Open | OpenApache 2.0 |
| API model ID | glm-4.7 | jamba-large | — |
| API providers | 31 (best) | 1 | 12 |
| Released | Dec 22, 2025 | Jul 3, 2025 | Jul 25, 2025 |
| Knowledge cutoff | Apr 2025 | Aug 22, 2024 | — |
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
- Jamba Large$36.00
- Qwen3 235B-A22B Thinking 2507$8.80
Which should you choose?
Which is better: GLM-4.7, Jamba Large or Qwen3 235B-A22B Thinking 2507?
It is close. Our weighted score puts them within 1 points (Qwen3 235B-A22B Thinking 2507 42/100, GLM-4.7 41/100, Jamba Large 29/100), so choose by what matters most for your work: Qwen3 235B-A22B Thinking 2507 on price and Qwen3 235B-A22B Thinking 2507 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, Jamba Large or Qwen3 235B-A22B Thinking 2507?
Qwen3 235B-A22B Thinking 2507 is cheaper at $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); Jamba Large costs $2.00 input / $8.00 output per million tokens (official AI21 Labs API price). At a typical mix of three input tokens to one output token, that is $0.95 per million tokens for Qwen3 235B-A22B Thinking 2507 versus $1.00 for GLM-4.7 (1.1× as much) and $3.50 for Jamba Large (3.7× 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, Jamba Large has not been scored yet and Qwen3 235B-A22B Thinking 2507 has an ECI of 143.8.
Which is better for coding?
There are no published SWE-bench Verified results for GLM-4.7, Jamba Large and Qwen3 235B-A22B Thinking 2507 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 256,000 for Jamba Large and 204,800 for GLM-4.7. Maximum output per response: GLM-4.7 up to 131,072, Jamba Large up to 4,096, Qwen3 235B-A22B Thinking 2507 up to 81,920 tokens.
Which can read images, PDFs, audio or video?
GLM-4.7 accepts text; Jamba Large accepts text; Qwen3 235B-A22B Thinking 2507 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; Jamba Large came out Jul 3, 2025. Knowledge cutoff: GLM-4.7 Apr 2025, Jamba Large Aug 22, 2024.
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.
