Gemma 4 26B A4B IT vs GPT-5 Nano vs Qwen3.5 Flash
Qwen3.5 Flash comes out ahead, 75 to 70 and 70 on our weighted score, though GPT-5 Nano is 21% cheaper per token.
GoogleGemma 4 26B A4B IT
70/100- ECI141.9
- Price$0.10 / $0.38
- Context262K
OpenAIGPT-5 Nano
70/100- ECI139.4
- Price$0.05 / $0.40
- 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 GPT-5 Nano (70) and Gemma 4 26B A4B IT (70). It leads on capability, inputs & features and context window. GPT-5 Nano wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityQwen3.5 FlashCapabilities Index (ECI): Qwen3.5 Flash 144.0 · Gemma 4 26B A4B IT 141.9 · GPT-5 Nano 139.4
- Lowest priceGPT-5 NanoGPT-5 Nano $0.138 · Gemma 4 26B A4B IT $0.17 · Qwen3.5 Flash $0.175 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 FlashQwen3.5 Flash 1,000,000 · GPT-5 Nano 400,000 · Gemma 4 26B A4B IT 262,144 tokens
- Widest inputsQwen3.5 FlashGemma 4 26B A4B IT: Text, Images · GPT-5 Nano: Text, Images · Qwen3.5 Flash: Text, Images, Video
- Self-hostingGemma 4 26B A4B ITPublishes downloadable weights
| Measure | Weight | Gemma 4 26B A4B IT | GPT-5 Nano | Qwen3.5 Flash |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 68 | 65 | 71 |
| Price | 25% | 86 | 91 | 86 |
| Inputs & features | 15% | 70 | 70 | 80 |
| Context window | 10% | 37 | 44 | 60 |
| Overall | 100% | 70/100 | 70/100 | 75/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Gemma 4 26B A4B ITGoogle | GPT-5 NanoOpenAI | Qwen3.5 FlashAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 141.9 | 139.4 | 144.0 (best) |
| ECI rank | #93 of 148 | #102 of 148 | #82 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 73.2% | 69.4% | 82.3% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | 20.0% (best) | 18.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | 82.2% | 81.1% | 84.4% (best) |
| SimpleQA VerifiedShort factual questions | — | 11.7% | 20.3% (best) |
| Price per million tokens | |||
| Input | $0.10 | $0.05 (best) | $0.10 |
| Output | $0.38 (best) | $0.40 | $0.40 |
| Cached input | — | $0.005 (best) | $0.01 |
| Blended (3:1) | $0.17 | $0.138 (best) | $0.175 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 23 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 | Yesminimal · low · medium · high | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | Yes |
| Availability | |||
| Weights | Open | Proprietary | Proprietary |
| API model ID | gemma-4-26b-a4b-it | gpt-5-nano | qwen3.5-flash |
| API providers | 26 (best) | 21 | 8 |
| Released | Apr 2, 2026 | Aug 7, 2025 | Feb 23, 2026 |
| Knowledge cutoff | — | May 30, 2024 | — |
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 26B A4B IT$1.76
- GPT-5 Nano$1.30
- Qwen3.5 Flash$1.80
Which should you choose?
Which is better: Gemma 4 26B A4B IT, GPT-5 Nano or Qwen3.5 Flash?
Qwen3.5 Flash is the better all-round choice, scoring 75/100 against GPT-5 Nano (70) and Gemma 4 26B A4B IT (70). It leads on capability, inputs & features and context window. GPT-5 Nano wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, Gemma 4 26B A4B IT, GPT-5 Nano or Qwen3.5 Flash?
GPT-5 Nano is cheaper at $0.05 input / $0.40 output per million tokens (official OpenAI API price). Gemma 4 26B A4B IT costs $0.10 input / $0.38 output per million tokens (median across 23 API providers); Qwen3.5 Flash costs $0.10 input / $0.40 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.138 per million tokens for GPT-5 Nano versus $0.17 for Gemma 4 26B A4B IT (1.2× as much) and $0.175 for Qwen3.5 Flash (1.3× as much).
Which scores higher on benchmarks?
Qwen3.5 Flash scores higher on the Capabilities Index (ECI): Qwen3.5 Flash 144.0 (#82 of 148), Gemma 4 26B A4B IT 141.9 (#93 of 148) and GPT-5 Nano 139.4 (#102 of 148). The confidence ranges of the top two overlap (141.6–145.5 vs 138.4–143.4), so treat the gap as small. On individual benchmarks: GPQA Diamond — Qwen3.5 Flash 82.3%, Gemma 4 26B A4B IT 73.2%, GPT-5 Nano 69.4%; OTIS Mock AIME 2024–2025 — Qwen3.5 Flash 84.4%, Gemma 4 26B A4B IT 82.2%, GPT-5 Nano 81.1%.
Which is better for coding?
There are no published SWE-bench Verified results for Gemma 4 26B A4B IT, GPT-5 Nano and Qwen3.5 Flash yet, so there is no like-for-like coding score. On overall capability, Qwen3.5 Flash 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 Nano and 262,144 for Gemma 4 26B A4B IT. Maximum output per response: Gemma 4 26B A4B IT up to 32,768, GPT-5 Nano up to 128,000, Qwen3.5 Flash up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Gemma 4 26B A4B IT accepts text and images; GPT-5 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 26B A4B IT publishes its weights and can be self-hosted; GPT-5 Nano and Qwen3.5 Flash is proprietary.
Which is newer?
Gemma 4 26B A4B IT is the newest, released Apr 2, 2026. Qwen3.5 Flash came out Feb 23, 2026; GPT-5 Nano came out Aug 7, 2025. Knowledge cutoff: GPT-5 Nano May 30, 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 Oct 4, 2026.
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