Gemma 4 31B IT vs GPT-5.6 Cyber vs Qwen3.5 Flash
Qwen3.5 Flash comes out ahead, 79 to 70 and 30 on our weighted score, and it is the cheaper option too.
GoogleGemma 4 31B IT
70/100- ECI142.8
- Price$0.14 / $0.40
- Context262K
OpenAIGPT-5.6 Cyber
30/100- ECI—
- Price$12.50 / $75.00
- Context400K
- Our pick
Alibaba (Qwen)Qwen3.5 Flash
79/100- ECI144.0
- Price$0.10 / $0.40
- Context1M
Qwen3.5 Flash is our pick
Qwen3.5 Flash is the better all-round choice, scoring 79/100 against Gemma 4 31B IT (70) and GPT-5.6 Cyber (30). It leads on price, inputs & features and 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 priceQwen3.5 FlashQwen3.5 Flash $0.175 · Gemma 4 31B IT $0.205 · GPT-5.6 Cyber $28.13 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 FlashQwen3.5 Flash 1,000,000 · GPT-5.6 Cyber 400,000 · Gemma 4 31B IT 262,144 tokens
- Widest inputsQwen3.5 FlashGemma 4 31B IT: Text, Images · GPT-5.6 Cyber: Text, Images · Qwen3.5 Flash: Text, Images, Video
- Self-hostingGemma 4 31B ITPublishes downloadable weights
| Measure | Weight | Gemma 4 31B IT | GPT-5.6 Cyber | Qwen3.5 Flash |
|---|---|---|---|---|
| Price | 50% | 83 | 0 | 86 |
| Inputs & features | 30% | 70 | 70 | 80 |
| Context window | 20% | 37 | 44 | 60 |
| Overall | 100% | 70/100 | 30/100 | 79/100 |
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 | Gemma 4 31B ITGoogle | GPT-5.6 CyberOpenAI | Qwen3.5 FlashAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 142.8 | — | 144.0 (best) |
| ECI rank | #86 of 148 | — | #82 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 75.8% | — | 82.3% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 18.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | 73.3% | — | 84.4% (best) |
| SimpleQA VerifiedShort factual questions | 10.4% | — | 20.3% (best) |
| Price per million tokens | |||
| Input | $0.14 | $12.50 | $0.10 (best) |
| Output | $0.40 (best) | $75.00 | $0.40 (best) |
| Cached input | — | $1.25 | $0.01 (best) |
| Blended (3:1) | $0.205 | $28.13 | $0.175 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 30 providers | Official OpenAI API | Official Alibaba API |
| Limits | |||
| Context window | 262,144 tokens | 400,000 tokens | 1,000,000 tokens (best) |
| Max output | 32,768 tokens | 128,000 tokens (best) | 65,536 tokens |
| 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 | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | gemma-4-31b-it | gpt-daybreak-red-latest | qwen3.5-flash |
| API providers | 38 (best) | 1 | 8 |
| Released | Apr 2, 2026 | Aug 7, 2026 | Feb 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.
- Gemma 4 31B IT$2.20
- GPT-5.6 Cyber$275.00
- Qwen3.5 Flash$1.80
Which should you choose?
Which is better: Gemma 4 31B IT, GPT-5.6 Cyber or Qwen3.5 Flash?
Qwen3.5 Flash is the better all-round choice, scoring 79/100 against Gemma 4 31B IT (70) and GPT-5.6 Cyber (30). It leads on price, inputs & features and 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, Gemma 4 31B IT, GPT-5.6 Cyber or Qwen3.5 Flash?
Qwen3.5 Flash is cheaper at $0.10 input / $0.40 output per million tokens (official Alibaba API price). Gemma 4 31B IT costs $0.14 input / $0.40 output per million tokens (median across 30 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 $0.175 per million tokens for Qwen3.5 Flash versus $0.205 for Gemma 4 31B IT (1.2× as much) and $28.13 for GPT-5.6 Cyber (161× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Gemma 4 31B IT has an ECI of 142.8, GPT-5.6 Cyber has not been scored yet and Qwen3.5 Flash has an ECI of 144.0.
Which is better for coding?
There are no published SWE-bench Verified results for Gemma 4 31B IT, GPT-5.6 Cyber and Qwen3.5 Flash 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?
Qwen3.5 Flash has the largest context window at 1,000,000 tokens, against 400,000 for GPT-5.6 Cyber and 262,144 for Gemma 4 31B IT. Maximum output per response: Gemma 4 31B IT up to 32,768, GPT-5.6 Cyber up to 128,000, Qwen3.5 Flash up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Gemma 4 31B IT accepts text and images; GPT-5.6 Cyber accepts text and images; Qwen3.5 Flash accepts text, images and video. Qwen3.5 Flash handles the widest range of inputs.
Are any of these open source?
Gemma 4 31B IT publishes its weights and can be self-hosted; GPT-5.6 Cyber and Qwen3.5 Flash is proprietary.
Which is newer?
GPT-5.6 Cyber is the newest, released Aug 7, 2026. Gemma 4 31B IT came out Apr 2, 2026; Qwen3.5 Flash came out Feb 23, 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 Oct 4, 2026.
