Ember-1 vs GPT-5.6 Cyber vs Qwen 3.8 Max Prime
Too close to call on our weighted score (Qwen 3.8 Max Prime 39, Ember-1 36, GPT-5.6 Cyber 30). The right pick depends on what you value most.
Fireworks AIEmber-1
36/100- ECI—
- Price$3.00 / $15.00
- Context1.05M
OpenAIGPT-5.6 Cyber
30/100- ECI—
- Price$12.50 / $75.00
- Context400K
Alibaba (Qwen)Qwen 3.8 Max Prime
39/100- ECI—
- Price$4.00 / $12.00
- Context1M
Too close to call
It is close. Our weighted score puts them within 3 points (Qwen 3.8 Max Prime 39/100, Ember-1 36/100, GPT-5.6 Cyber 30/100), so choose by what matters most for your work: Ember-1 on price and Ember-1 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 priceEmber-1 and Qwen 3.8 Max PrimeEmber-1 $6.00 · Qwen 3.8 Max Prime $6.00 · GPT-5.6 Cyber $28.13 per 1M tokens (3:1 blend)
- Longest contextEmber-1Ember-1 1,048,576 · Qwen 3.8 Max Prime 1,000,000 · GPT-5.6 Cyber 400,000 tokens
- Widest inputsQwen 3.8 Max PrimeEmber-1: Text, Images · GPT-5.6 Cyber: Text, Images · Qwen 3.8 Max Prime: Text, Images, Video
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | Ember-1 | GPT-5.6 Cyber | Qwen 3.8 Max Prime |
|---|---|---|---|---|
| Price | 50% | 12 | 0 | 12 |
| Inputs & features | 30% | 60 | 70 | 70 |
| Context window | 20% | 61 | 44 | 60 |
| Overall | 100% | 36/100 | 30/100 | 39/100 |
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 | Ember-1Fireworks AI | GPT-5.6 CyberOpenAI | Qwen 3.8 Max PrimeAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $3.00 (best) | $12.50 | $4.00 |
| Output | $15.00 | $75.00 | $12.00 (best) |
| Cached input | — | $1.25 | — |
| Blended (3:1) | $6.00 (best) | $28.13 | $6.00 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 7 providers | Official OpenAI API | Median of 5 providers |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 400,000 tokens | 1,000,000 tokens |
| Max output | 131,072 tokens (best) | 128,000 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yes | Yeslow · medium · high · xhigh · max | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Proprietary |
| API model ID | — | gpt-daybreak-red-latest | — |
| API providers | 7 (best) | 1 | 5 |
| Released | Sep 22, 2026 | Aug 7, 2026 | Sep 23, 2026 |
| Knowledge cutoff | — | Feb 16, 2026 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Ember-1$60.00
- GPT-5.6 Cyber$275.00
- Qwen 3.8 Max Prime$64.00
Which should you choose?
Which is better: Ember-1, GPT-5.6 Cyber or Qwen 3.8 Max Prime?
It is close. Our weighted score puts them within 3 points (Qwen 3.8 Max Prime 39/100, Ember-1 36/100, GPT-5.6 Cyber 30/100), so choose by what matters most for your work: Ember-1 on price and Ember-1 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, Ember-1, GPT-5.6 Cyber or Qwen 3.8 Max Prime?
Ember-1 is cheaper at $3.00 input / $15.00 output per million tokens (median across 7 API providers). Qwen 3.8 Max Prime costs $4.00 input / $12.00 output per million tokens (median across 5 API providers); GPT-5.6 Cyber costs $12.50 input / $75.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $6.00 per million tokens for Ember-1 versus $6.00 for Qwen 3.8 Max Prime (1× as much) and $28.13 for GPT-5.6 Cyber (4.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Ember-1 has not been scored yet, GPT-5.6 Cyber has not been scored yet and Qwen 3.8 Max Prime has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Ember-1, GPT-5.6 Cyber and Qwen 3.8 Max Prime 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?
Ember-1 has the largest context window at 1,048,576 tokens, against 1,000,000 for Qwen 3.8 Max Prime and 400,000 for GPT-5.6 Cyber. Maximum output per response: Ember-1 up to 131,072, GPT-5.6 Cyber up to 128,000, Qwen 3.8 Max Prime up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Ember-1 accepts text and images; GPT-5.6 Cyber accepts text and images; Qwen 3.8 Max Prime accepts text, images and video. Qwen 3.8 Max Prime handles the widest range of inputs.
Are any of these open source?
No. Ember-1, GPT-5.6 Cyber and Qwen 3.8 Max Prime are proprietary and only available through APIs and apps.
Which is newer?
Qwen 3.8 Max Prime is the newest, released Sep 23, 2026. Ember-1 came out Sep 22, 2026; GPT-5.6 Cyber came out Aug 7, 2026. Knowledge cutoff: GPT-5.6 Cyber Feb 16, 2026.
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.
