FreeLLMAll models › deepseek
Where to use it for free
- Right here, no account: ask it a question below (5 free questions a day)
- The lab's own chat: chat.deepseek.com (free with daily limits)
- Download and run it at home: official weights · GGUF files for Ollama and LM Studio
Try DeepSeek-V4-Pro now
No account, no payment. 5 questions a day per visitor, short answers. It runs on Cloudflare Workers AI (@cf/deepseek-ai/deepseek-v4-pro-0813). We do not store your question. Do not write personal data.
Arena scores
| Arena | Score | Rank | Votes |
|---|---|---|---|
| Chat | 1451 | #49 of 417 | 58K |
| Coding | 1582 | #36 of 148 | 5K |
| Agents | 0.1 agent score | #30 of 53 | 7K sessions |
Rank among all models, paid ones included. Source: Arena leaderboard dataset. The agent score is on a different scale (×100, from real agent sessions) and cannot be compared with the other scores.
Speed on popular hardware
| Hardware | GPU memory | Fits? | Speed |
|---|---|---|---|
| RTX 3060 12 GB | 12 GB | Does not fit | — |
| RTX 4060 Ti 16 GB | 16 GB | Does not fit | — |
| RTX 3090 | 24 GB | Does not fit | — |
| RTX 4090 | 24 GB | Does not fit | — |
| RTX 5090 | 32 GB | Does not fit | — |
| RX 7900 XTX | 24 GB | Does not fit | — |
| 2 × RTX 3090 | 48 GB | Does not fit | — |
| 4 × RTX 3090 | 96 GB | Does not fit | — |
| Mac M4 Max 128 GB | 96 GB | Does not fit | — |
| Ryzen AI Max+ 395 128 GB | 96 GB | Does not fit | — |
| 8 × H100 SXM | 640 GB | Does not fit | — |
Estimate at 4-bit with 32K context, 64 GB of DDR5 for offload. Speed ≈ memory bandwidth × 78% ÷ data read per token, plus a fixed cost per token, calibrated on 44 real llama.cpp tests; typical error about ±10%. Compare all GPUs.
GPU memory needed
| Level | 8K context | 32K context | 128K context | Quality vs full |
|---|---|---|---|---|
| Q3_K_M | 801 GB | 804 GB | 816 GB | +4.7% noticeable loss |
| Q4_K_M | 970 GB | 974 GB | 986 GB | +1.2% very small loss |
| Q5_K_M | 1138 GB | 1141 GB | 1154 GB | +0.4% near-lossless |
| Q6_K | 1313 GB | 1316 GB | 1328 GB | +0.2% near-lossless |
| Q8_0 | 1700 GB | 1703 GB | 1716 GB | ≈ 0% same as full |
| F16 | 3199 GB | 3202 GB | 3215 GB | ≈ 0% same as full |
Weights + context cache (from the model's architecture) + runtime. Quality: increase in perplexity against the full model, from llama.cpp tests, scaled by model size. Simulate your own setup.
Coding score over time
Arena score from 20 Apr 2026 to 19 Oct 2026.
Agents score over time
Arena score from 4 Jun 2026 to 20 Oct 2026.
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