Gemma 4 31B IT vs GPT-5.4 nano vs Qwen3.5 Flash
Qwen3.5 Flash comes out ahead, 75 to 69 and 68 on our weighted score, and it is the cheaper option too.
Google
Gemma 4 31B IT
69/100- ECI142.8
- Price$0.14 / $0.40
- Context262K
OpenAI
GPT-5.4 nano
68/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
- Our pick
Alibaba (Qwen)
Qwen3.5 Flash
75/100- ECI144.0
- Price$0.10 / $0.40
- Context1M
Qwen3.5 Flash is our pick
Qwen3.5 Flash is the better all-round choice, scoring 75/100 against Gemma 4 31B IT (69) and GPT-5.4 nano (68). It leads on price, inputs & features and context window. GPT-5.4 nano wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT-5.4 nanoCapabilities Index (ECI): GPT-5.4 nano 145.8 · Qwen3.5 Flash 144.0 · Gemma 4 31B IT 142.8
- Lowest priceQwen3.5 FlashQwen3.5 Flash $0.175 · Gemma 4 31B IT $0.205 · GPT-5.4 nano $0.463 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 FlashQwen3.5 Flash 1,000,000 · GPT-5.4 nano 400,000 · Gemma 4 31B IT 262,144 tokens
- Widest inputsQwen3.5 FlashGemma 4 31B IT: Text, Images · GPT-5.4 nano: Text, Images · Qwen3.5 Flash: Text, Images, Video
- Self-hostingGemma 4 31B ITPublishes downloadable weights
| Measure | Weight | Gemma 4 31B IT | GPT-5.4 nano | Qwen3.5 Flash |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 69 | 73 | 71 |
| Price | 25% | 83 | 66 | 86 |
| Inputs & features | 15% | 70 | 70 | 80 |
| Context window | 10% | 37 | 44 | 60 |
| Overall | 100% | 69/100 | 68/100 | 75/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | |||
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 142.8 | 145.8 (best) | 144.0 |
| ECI rank | #86 of 148 | #75 of 148 (best) | #82 of 148 |
| GPQA DiamondGraduate-level science questions | 75.8% | 78.5% | 82.3% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | 44.9% (best) | 18.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | 73.3% | 87.8% (best) | 84.4% |
| SimpleQA VerifiedShort factual questions | 10.4% | 11.7% | 20.3% (best) |
| Price per million tokens | |||
| Input | $0.14 | $0.20 | $0.10 (best) |
| Output | $0.40 (best) | $1.25 | $0.40 (best) |
| Cached input | — | $0.02 | $0.01 (best) |
| Blended (3:1) | $0.205 | $0.463 | $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 | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | gemma-4-31b-it | gpt-5.4-nano | qwen3.5-flash |
| API providers | 38 (best) | 26 | 8 |
| Released | Apr 2, 2026 | Mar 17, 2026 | Feb 23, 2026 |
| Knowledge cutoff | — | Aug 31, 2025 | — |
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-5.4 nano$4.50
Qwen3.5 Flash$1.80
Which should you choose?
Which is better: Gemma 4 31B IT, GPT-5.4 nano or Qwen3.5 Flash?
Qwen3.5 Flash is the better all-round choice, scoring 75/100 against Gemma 4 31B IT (69) and GPT-5.4 nano (68). It leads on price, inputs & features and context window. GPT-5.4 nano wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Gemma 4 31B IT, GPT-5.4 nano 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-5.4 nano costs $0.20 input / $1.25 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 $0.463 for GPT-5.4 nano (2.6× as much).
Which scores higher on benchmarks?
GPT-5.4 nano scores higher on the Capabilities Index (ECI): GPT-5.4 nano 145.8 (#75 of 148), Qwen3.5 Flash 144.0 (#82 of 148) and Gemma 4 31B IT 142.8 (#86 of 148). The confidence ranges of the top two overlap (143.2–147.7 vs 141.6–145.5), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.5 Flash 82.3%, GPT-5.4 nano 78.5%, Gemma 4 31B IT 75.8%; OTIS Mock AIME 2024–2025 — GPT-5.4 nano 87.8%, Qwen3.5 Flash 84.4%, Gemma 4 31B IT 73.3%; SimpleQA Verified — Qwen3.5 Flash 20.3%, GPT-5.4 nano 11.7%, Gemma 4 31B IT 10.4%.
Which is better for coding?
There are no published SWE-bench Verified results for Gemma 4 31B IT, GPT-5.4 nano and Qwen3.5 Flash yet, so there is no like-for-like coding score. On overall capability, GPT-5.4 nano leads, which tends to carry over to coding, but test on your own codebase. 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-5.4 nano and 262,144 for Gemma 4 31B IT. Maximum output per response: Gemma 4 31B IT up to 32,768, GPT-5.4 nano 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-5.4 nano 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-5.4 nano and Qwen3.5 Flash is proprietary.
Which is newer?
Gemma 4 31B IT is the newest, released Apr 2, 2026. GPT-5.4 nano came out Mar 17, 2026; Qwen3.5 Flash came out Feb 23, 2026. Knowledge cutoff: GPT-5.4 nano Aug 31, 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.