Franky Bonanno

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BenchMark DeepSeek V4.1 Flash vs Sonnet 5.5

BenchMark DeepSeek V4.1 Flash vs Sonnet 5.5

Summary: DeepSeek V4.1 Flash

🆔 Identity and Version

  • Model: DeepSeek V4.1 Flash
  • Developer: DeepSeek
  • Release: V4.1-Flash-1017, updated on 17 October 2026
  • Type: language model (not multimodal for image output)
  • Positioning: a “Flash” model focused on efficiency and low cost, ideal for high-volume applications

🧠 Capabilities and Limitations

  • It does not create or directly manipulate images: it does not generate graphic files and does not edit pixels.
  • However, it can:
  • write effective prompts for image generators (Midjourney, DALL·E, Stable Diffusion, etc.);
  • describe or analyse an image if uploaded in the interface, within the platform’s limits;
  • suggest tools and procedures for editing, photo retouching, or automation;
  • write scripts (Python, ImageMagick, etc.) to manipulate images.

⚖️ Comparison with Claude Sonnet 5.5

Feature | Claude Sonnet 5.5 | DeepSeek V4.1 Flash

Developer

Anthropic | DeepSeek

  • Overall score (BenchLM.ai) | ~82.17/100 | ~65.59/100
  • Agentic coding (Terminal-Bench 4.0) | 70.6% (best in the family) | Lower
  • Tool use | ~69.3 | ~61.9
  • Input price | $2/M tokens | $0.018/M tokens
  • Output price | $10/M tokens | $0.684/M tokens
  • Input cost difference | — | About 93% cheaper
  • First-token latency (p50) | 2.88 s | 2.15 s
  • Throughput | 112 tokens/s | 15 tokens/s
  • Context window | 1,000,000 tokens | 1,048,576 tokens
  • Input modalities | Text and images | Text only

In short: Sonnet 5.5 is generally more powerful and precise, especially in coding and complex tasks; DeepSeek V4.1 Flash is far cheaper and better suited to scale, with lower initial latency.

📄 Tokens and Pages

  • 1 million tokens corresponds roughly to 800–900 dense A4 pages at 11 pt in Italian, with a realistic range of 700–1,100 pages depending on language, tokenizer, and text density.
  • For a 1,000-page PDF, the estimate is about 1.5 million tokens.
  • Estimated cost for 1,000 pages:
  • Sonnet 5.5: about $3.00
  • DeepSeek V4.1 Flash: about $0.08–$0.45 depending on the provider

📦 Size and Open Source

  • Open source: yes, weights released under the MIT licence (use, copying, modification, and distribution permitted, including commercially).
  • Parameters: architecture with 552 billion parameters in the backbone + 196 billion in the “Engram” component → over 750 billion in total.
  • Disk size:
  • Full BF16 version: about 510 GB
  • 8-bit: about 508 GB
  • 4-bit: about 500 GB
  • 1-bit: about 250 GB

🔢 Quantization: 8-bit vs 1-bit

8-bit (Q8)

  • Quality: almost “lossless”; output typically indistinguishable from the full-precision (FP16) model. Perplexity increase usually < 1%.
  • Size: about 508 GB.
  • Trade-off: maximum quality outside the original model.

1-bit (Q1)

  • Quality: clear degradation, but advanced versions (e.g. UD-IQ1_M) retain a top-1 agreement of 88.2% with the original model. The loss mainly affects reasoning subtlety and accuracy.
  • Size: about 250 GB.
  • Trade-off: accessibility on consumer hardware.

In short: with 8-bit you pay in storage for near-original quality; with 1-bit you lose some precision but make the model runnable on less powerful machines.

💻 Hardware Requirements (minimum RAM + VRAM, according to Unsloth)

Quantization | Minimum memory required

  • 1-bit | 128–145 GB
  • 2-bit | 176–205 GB
  • 3-bit | 256–290 GB
  • 4-bit | 305 GB
  • 8-bit | 315 GB
  • 1-bit: designed for systems with 128 GB of RAM (high-end MacBook, NVIDIA DGX Spark, RTX Spark). There are demonstrations of execution on a Mac mini M1 with 16 GB of unified memory, but with very low performance.
  • 4-bit and 8-bit: for a realistic service you need 4–8 80 GB-class GPUs (e.g. A100) or a workstation with at least 512 GB of system RAM and one or two GPUs, with dynamic transfer of expert layers to the GPU when needed.

🧠 Running Without VRAM (RAM/CPU only)

  • Yes, it is possible. The model is designed to run on CPU, relying on system RAM and SSD streaming.
  • The “Engram” tables can be kept on the SSD and loaded on demand into the operating system cache, saving RAM.
  • Performance: much slower than GPU execution, on the order of 1 token/s or less on CPU-only setups. Suitable for testing, development, or personal use without time pressure, not for low-latency services.

💎 Brief Summary

  • DeepSeek V4.1 Flash is an open-source (MIT), text-only model with a context window of over 1 million tokens.
  • It is less powerful than Claude Sonnet 5.5 on complex benchmarks and agentic coding, but costs about 93% less on input.
  • Quantization is the main accessibility tool: 8-bit for near-original quality, 1-bit for execution on consumer hardware (128 GB of RAM).
  • It can run without VRAM, using only RAM and SSD, but with very low throughput.