GPT-5 Chat vs Jamba Large vs Qwen3-Coder 480B-A35B Instruct
GPT-5 Chat comes out ahead, 34 to 30 and 28 on our weighted score, though Qwen3-Coder 480B-A35B Instruct is 13% cheaper per token.
- Our pick
OpenAIGPT-5 Chat
34/100- ECI—
- Price$1.25 / $10.00
- Context400K
AI21 LabsJamba Large
30/100- ECI—
- Price$2.00 / $8.00
- Context256K
Alibaba (Qwen)Qwen3-Coder 480B-A35B Instruct
28/100- ECI—
- Price$1.50 / $7.50
- Context262K
GPT-5 Chat is our pick
GPT-5 Chat is the better all-round choice, scoring 34/100 against Jamba Large (30) and Qwen3-Coder 480B-A35B Instruct (28). It leads on inputs & features and context window. Qwen3-Coder 480B-A35B Instruct wins on price. 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-Coder 480B-A35B InstructQwen3-Coder 480B-A35B Instruct $3.00 · GPT-5 Chat $3.44 · Jamba Large $3.50 per 1M tokens (3:1 blend)
- Longest contextGPT-5 ChatGPT-5 Chat 400,000 · Qwen3-Coder 480B-A35B Instruct 262,144 · Jamba Large 256,000 tokens
- Widest inputsGPT-5 ChatGPT-5 Chat: Text, Images · Jamba Large: Text · Qwen3-Coder 480B-A35B Instruct: Text
- Self-hostingJamba Large and Qwen3-Coder 480B-A35B InstructPublishes downloadable weights
| Measure | Weight | GPT-5 Chat | Jamba Large | Qwen3-Coder 480B-A35B Instruct |
|---|---|---|---|---|
| Price | 50% | 24 | 24 | 27 |
| Inputs & features | 30% | 45 | 35 | 25 |
| Context window | 20% | 44 | 36 | 37 |
| Overall | 100% | 34/100 | 30/100 | 28/100 |
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 | GPT-5 ChatOpenAI | Jamba LargeAI21 Labs | Qwen3-Coder 480B-A35B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.25 (best) | $2.00 | $1.50 |
| Output | $10.00 | $8.00 | $7.50 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $3.44 | $3.50 | $3.00 (best) |
| Long-context rate | Same rate | Same rate | Over 32K: $2.70 / $13.50 |
| Price source | Median of 2 providers | Official AI21 Labs API | Official Alibaba API |
| Limits | |||
| Context window | 400,000 tokens (best) | 256,000 tokens | 262,144 tokens |
| Max output | 128,000 tokens (best) | 4,096 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | No | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Open | Open |
| API model ID | — | jamba-large | qwen3-coder-480b-a35b-instruct |
| API providers | 2 | 1 | 7 (best) |
| Released | Aug 7, 2025 | Jul 1, 2025 | Apr 2025 |
| Knowledge cutoff | Sep 30, 2024 | Aug 22, 2024 | 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.
- GPT-5 Chat$32.50
- Jamba Large$36.00
- Qwen3-Coder 480B-A35B Instruct$30.00
Which should you choose?
Which is better: GPT-5 Chat, Jamba Large or Qwen3-Coder 480B-A35B Instruct?
GPT-5 Chat is the better all-round choice, scoring 34/100 against Jamba Large (30) and Qwen3-Coder 480B-A35B Instruct (28). It leads on inputs & features and context window. Qwen3-Coder 480B-A35B Instruct wins on price. 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, GPT-5 Chat, Jamba Large or Qwen3-Coder 480B-A35B Instruct?
Qwen3-Coder 480B-A35B Instruct is cheaper at $1.50 input / $7.50 output per million tokens (official Alibaba API price). GPT-5 Chat costs $1.25 input / $10.00 output per million tokens (median across 2 API providers); 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 $3.00 per million tokens for Qwen3-Coder 480B-A35B Instruct versus $3.44 for GPT-5 Chat (1.1× as much) and $3.50 for Jamba Large (1.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5 Chat has not been scored yet, Jamba Large has not been scored yet and Qwen3-Coder 480B-A35B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5 Chat, Jamba Large and Qwen3-Coder 480B-A35B Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that GPT-5 Chat does not support tool calling, which most coding agents need.
Which has the bigger context window?
GPT-5 Chat has the largest context window at 400,000 tokens, against 262,144 for Qwen3-Coder 480B-A35B Instruct and 256,000 for Jamba Large. Maximum output per response: GPT-5 Chat up to 128,000, Jamba Large up to 4,096, Qwen3-Coder 480B-A35B Instruct up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GPT-5 Chat accepts text and images; Jamba Large accepts text; Qwen3-Coder 480B-A35B Instruct accepts text. GPT-5 Chat handles the widest range of inputs.
Are any of these open source?
Jamba Large and Qwen3-Coder 480B-A35B Instruct publishes its weights and can be self-hosted; GPT-5 Chat is proprietary.
Which is newer?
GPT-5 Chat is the newest, released Aug 7, 2025. Jamba Large came out Jul 1, 2025; Qwen3-Coder 480B-A35B Instruct came out Apr 2025. Knowledge cutoff: GPT-5 Chat Sep 30, 2024, Jamba Large Aug 22, 2024, Qwen3-Coder 480B-A35B Instruct 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 Oct 4, 2026.
