LLM Comparison
Cactus Needle 2 vs DiffusionGemma
Side-by-side specs, pricing & capabilities · Updated August 2026
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2/6 modelsSame tier:
| Organization | ||
| OpenTools Score | 36 | |
| Family | Needle | Gemma |
| Status | Current | Current |
| Release Date | Aug 2026 | Jun 2026 |
| Context Window | 256 tokens | 256K tokens |
| Input Price | Free | Free |
| Output Price | Free | Free |
| Pricing Notes | Apache 2.0 model artifact; runtime cost depends on local hardware, not token API pricing. | Open-weights model under Apache 2.0; API pricing depends on the host or local infrastructure used. |
| Capabilities | tool-usestructured-outputjson-schemadevice-controledge-inferenceoffline | textvisioncodereasoninglocal-inference |
| Max Output | 256 tokens | 256 tokens |
| API Identifier | Cactus-Compute/needle2 | google/diffusiongemma-26b-a4b-it |
| Benchmarks | ||
| Mobile Actions Ordered Strict Exact Match | 63.7cactus-compute | — |
| BFCL v4 Single-Turn Overall | 42.6cactus-compute | — |
| MMLU Pro | — | 77.6official-google-model-card |
| GPQA Diamond | — | 73.2official-google-model-card |
| LiveCodeBench v6 | — | 69.1official-google-model-card |
| MMMLU | — | 81.5official-google-model-card |
| HLE no tools | — | 11official-google-model-card |
| View Cactus Needle 2 | View DiffusionGemma | |
Cost Calculator
Enter your expected monthly token usage to compare costs.
| Model | Input | Output | Total / mo | vs Best |
|---|---|---|---|---|
| Cactus Needle 2Cheapest | $0.00 | $0.00 | $0.00 | — |
| DiffusionGemmaCheapest | $0.00 | $0.00 | $0.00 | — |
Cactus Compute
Cactus Needle 2
Cactus Needle 2 is a 45M-parameter, 14 MB agentic model for tiny devices. It focuses on tool calling, device control, schema-constrained JSON, and structured extraction; Cactus reports 28 MB session RAM, 800+ tok/s prefill, and 500+ tok/s decode on Raspberry Pi 5.
DiffusionGemma
DiffusionGemma is Google DeepMind’s experimental open-weights text-diffusion model based on Gemma 4 26B A4B. It uses discrete diffusion and parallel canvas denoising to trade some benchmark quality for much faster local generation on dedicated GPUs.
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