DeepSeek vs Claude (2026): V4-Pro and V4.1-Flash vs Opus 5.5, Sonnet 5.5 and Haiku 5.5 Compared

DeepSeek or Claude: which one should you actually use in November 2026? Both companies shipped a lot this autumn. Anthropic released Claude Fable 5.1, Opus 5.5, Sonnet 5.5 and Haiku 5.5 between September and early October, and DeepSeek answered with the V4-Pro 0813 flagship and the new DeepSeek V4.1-Flash-1017 on October 17. In this guide I compare the latest models from both sides on benchmarks, API pricing, context length, open weights and, because this is a local AI site, what you can really run on your own PC. My own rig is two RTX 5080 cards (16 GB each) with 128 GB of system RAM, so the local section is written for that kind of desktop.

The Latest Models (November 2026)

Before comparing anything, it helps to know exactly which models are current, because both families moved fast in the last three months and a lot of comparison posts online still talk about DeepSeek V3 and Claude 4.

Claude lineup

• Claude Fable 5.1 (September 1, 2026): Anthropic’s most capable widely released model, aimed at the hardest reasoning and long agentic jobs. It is also the most expensive at $10 / $50 per million tokens.
• Claude Opus 5.5 (September 22, 2026): the main flagship and the default model in Anthropic’s API. It is cheaper than the Opus 5 it replaced ($4 / $20 instead of $5 / $25).
• Claude Sonnet 5.5 (September 28, 2026): the fast everyday model for coding, agents and business work, at $2 / $10. It even beats Opus 5.5 on Terminal-Bench 4.0.
• Claude Haiku 5.5 (October 7, 2026): the small, cheap model. Anthropic says it costs about 75% less to run on average than Haiku 4.5, at $0.10 / $0.50 per million tokens for prompts up to 100K tokens.

All four have a 1M token context window and accept text and images. None of them can be downloaded.

DeepSeek lineup

• DeepSeek V4-Pro 0813 (August 13, 2026): the flagship. It is a 1.6 trillion parameter mixture-of-experts model with 49B active parameters per token, trained on more than 32T tokens. It has non-thinking, high and max reasoning modes.
• DeepSeek V4-Flash 0731 (July 31, 2026): 284B total parameters with about 13B active. This is the one that hobbyists run at home.
• DeepSeek V4.1-Flash (September 10, 2026) and its update V4.1-Flash-1017 (October 17, 2026): the newest DeepSeek release. It uses a 552B backbone plus 196B “Engram” n-gram memory parameters, and DeepSeek says it gives stronger scores, faster task completion and shorter outputs than the previous Flash. It is served as deepseek-flash in the API at unchanged prices.
• DeepSeek V4.1-Pro: not released yet. As of the end of October 2026 DeepSeek had not given a date.

Every DeepSeek V4 and V4.1 model is released under the MIT license, with weights on Hugging Face. They are text only.

Specs Side by Side

The big structural difference is obvious from the table. Claude is a closed service: you rent it by the token and Anthropic runs it. DeepSeek publishes its weights, so you can use its cheap API, rent GPUs and host it yourself, or run the smaller Flash model on your own machine.

Benchmarks: DeepSeek V4-Pro vs Claude Opus 5.5

Benchmarks are where you have to be careful, because vendor numbers and independent numbers often disagree. DeepSeek’s own model card for V4-Pro 0813 claims SWE-bench Verified 80.6%, GPQA Diamond 90.1%, LiveCodeBench 93.5%, MMLU-Pro 87.5%, Terminal Bench 2.0 67.9% and a Codeforces rating of 3,206. Those are strong numbers, but they are self-reported and use older test versions.

For a fair, same-harness comparison I use the Artificial Analysis head-to-head page, which tests both models at their maximum reasoning setting with the same newer test suite:

Two things stand out. First, the gap on agentic work (Terminal-Bench 4.0, FrontierCode) is very large: this is the kind of work where you hand the model a repo or a terminal and let it work for many steps. Second, the AA-Omniscience score, which rewards correct answers and punishes confident wrong ones, is negative for DeepSeek. In plain words, V4-Pro is more likely to make things up when it does not know.

A note on the numbers: some older articles quote an Artificial Analysis score of 53 for V4-Pro and rank it third. That figure came from an earlier version of the index. The current index uses harder tests, which is why every model scores lower and the gap looks bigger now. I use only the current page here so both models are measured the same way.

Other Claude results worth knowing

• Opus 5.5: SWE-bench Pro 89.9%, OSWorld 2.1 (computer use) 81.8%, ARC-AGI-2 91.7%, HLE without tools 64.4% (benchlm.ai).
• Sonnet 5.5: 70.6% on Terminal-Bench 4.0, which is higher than Opus 5.5 on that test.
• Haiku 5.5: HLE 45.9% without tools and 57.4% with tools, OSWorld 2.1 72.4% (Anthropic’s launch table).

And the newest DeepSeek?

DeepSeek’s official table for V4.1-Flash-1017 shows big jumps over the previous Flash: HLE without tools 42.6 (from 36.8), Terminal-Bench 4.0 42.3 (from 31.2), DeepSWE 77.0 (from 74.2) and Automation-Bench 60.5 (from 54.8). Its Terminal-Bench score is much better than V4-Pro’s 14.1% on the Artificial Analysis run, but note these are DeepSeek’s own numbers, not the same harness. Even so, it is still well behind Haiku 5.5 on HLE without tools (45.9) and far behind Sonnet 5.5 and Opus 5.5 on Terminal-Bench.

API Pricing and Cost per Task

On the flagship tier DeepSeek is roughly 3x cheaper on input and 5x cheaper on output than Opus 5.5. Off-peak (DeepSeek’s night hours in China) it is 6x and 10x cheaper. The cost per task measured by Artificial Analysis ($1.77 vs $10.04) shows the real-world difference after you count how many tokens each model uses.

There is one surprise in the small tier. Claude Haiku 5.5 is now cheaper per token than DeepSeek Flash for prompts under 100K tokens ($0.10 vs $0.44 input, $0.50 vs $1.32 output). Above 100K tokens Haiku goes to $0.50 / $2.50, and DeepSeek’s cache-hit price of $0.014 is still very hard to beat for long, repeated prompts such as a big codebase or document you query again and again.

Also remember that Opus 5.5 has five effort levels. At “low” effort Artificial Analysis measured $0.98 per task with an index score of 35, which is still above V4-Pro’s 23 at max. So if your budget is tight, turning Claude’s effort down can be a better deal than switching vendors.

Where Each One Wins

Claude is better for

• Agentic coding: long tasks in a real repo with tests, tools and a terminal (Claude Code, Cline, Cursor). This is where the benchmark gap is largest.
• Computer use and automation: OSWorld and AutomationBench scores are clearly higher.
• Accuracy: fewer confident wrong answers, which matters for research and business writing.
• Images in the prompt: screenshots, charts, PDFs with figures. DeepSeek V4 is text only.
• Careful writing and long documents across a 1M token context.

DeepSeek is better for

• Price-sensitive bulk work: summarising, classifying, translation and chat at scale, especially with off-peak and cache-hit prices.
• Privacy and control: you can host the open weights yourself, so your data never leaves your server.
• Fine-tuning and research: MIT weights mean you can modify, distil and ship it in your own products.
• Offline use: V4-Flash runs on a high-RAM desktop, which no Claude model can do.
• Maths and competitive programming on the older public benchmarks (GPQA, LiveCodeBench, Codeforces), where its vendor scores are close to the top.

Can You Run DeepSeek or Claude Locally?

Claude: no. Anthropic does not release weights for any Claude model, so it only runs through claude.ai, the API, Amazon Bedrock, Google Cloud and Microsoft Foundry. Any “Claude GGUF” you see on Hugging Face is a fine-tune of another open model trained on Claude outputs, not Claude itself.

DeepSeek: yes, but only the Flash model on normal hardware. Here is what each needs, based on Unsloth’s published GGUF sizes:

For V4-Flash, a CPU-only test by ComputingForGeeks with the UD-IQ3_XXS file on mainline llama.cpp (build b10273) peaked at about 104 GiB of RAM and generated 5.6 tokens per second, with prompt processing at 12.3 tokens per second. With two 16 GB GPUs holding the attention layers and some experts, you should expect it to be faster than that, but I have not measured it on my PC, so treat that as an estimate. A 2-bit quant leaves more room for the KV cache, which grows with context length.

V4.1-Flash-1017 is a different story. Even its smallest 1-bit file is 233 GB. Unsloth’s trick is that the 110 GB Engram tables can be memory-mapped from a fast SSD, so the “rest” is about 122 GB, which only just fits 128 GB of RAM plus 32 GB of VRAM. It also needs a llama.cpp pull request branch (PR #30817) at the time of writing. I would wait for mainline support before trying it.

If you only have one 16 GB or 24 GB GPU and 32-64 GB of RAM, DeepSeek V4 is not for you locally. Look at the models in my Ollama install guide and use the DeepSeek API when you need it.

How to Run DeepSeek V4-Flash on a 128 GB PC

These steps use Unsloth’s GGUF and llama.cpp, which I prefer over Ollama for models this size because you get direct control over GPU offload. If you are new to llama.cpp, my Ollama vs LM Studio vs llama.cpp comparison explains why.

1. Get a recent llama.cpp with CUDA

Download the latest Windows CUDA build from the llama.cpp releases page (build b10273 or newer supports DeepSeek V4-Flash), or build it on Linux:

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j

2. Download the GGUF

Pick UD-Q2_K_XL (96.8 GB) for 128 GB RAM. It leaves room for Windows and the context cache.

pip install -U "huggingface_hub[cli]"
hf download unsloth/DeepSeek-V4-Flash-GGUF --include "*UD-Q2_K_XL*" --local-dir D:\models\DeepSeek-V4-Flash-GGUF

Keep all the shards in one folder and always point llama.cpp at the file that ends in -00001-of-0000N.gguf.

3. Start the server

llama-server -m D:\models\DeepSeek-V4-Flash-GGUF\UD-Q2_K_XL\<first-shard>.gguf ^
  --jinja --fit on -c 32768 --flash-attn on ^
  --temp 1.0 --top-p 1.0 --port 8080

• --fit on lets llama.cpp split the model across both GPUs and system RAM automatically. If you want manual control, use -ngl 99 --n-cpu-moe N and raise N until it stops running out of VRAM.
• -c 32768 is a safe start. Every extra token of context costs memory, so raise it slowly.
• If the output is garbled or repeats < characters, restart with --flash-attn off before changing anything else.
• The sampling values (temperature 1.0, top-p 1.0) follow Unsloth’s recommendation for the DeepSeek V4.1 family.

Open http://localhost:8080 for the built-in chat page, or point any OpenAI-compatible app (Cline, Open WebUI, Continue) at http://localhost:8080/v1. For more launch flags see my GGUF quantization guide, which also explains what IQ2 and Q2_K_XL mean.

Which Should You Choose?

A setup that works well is to mix them: run DeepSeek V4-Flash or another local model for private everyday tasks and bulk jobs, and send the hard agentic coding or high-stakes writing to Claude. Many coding tools (Cline, Continue, Open WebUI) let you switch models per task, so you only pay Claude prices where the quality matters.

Frequently Asked Questions

Is DeepSeek better than Claude?

Not on quality. On the current Artificial Analysis comparison, Claude Opus 5.5 beats DeepSeek V4-Pro 0813 on every test listed, with an Intelligence Index of 58 vs 23. DeepSeek is better on price and openness: it is several times cheaper and you can download its weights.

What is the latest DeepSeek model?

DeepSeek V4.1-Flash-1017, released October 17, 2026. The current flagship is still V4-Pro 0813. DeepSeek V4.1-Pro has been named but had no release date as of the end of October 2026.

What is the latest Claude model?

The newest release is Claude Haiku 5.5 (October 7, 2026). The most capable widely released model is Claude Fable 5.1, and the main flagship is Claude Opus 5.5.

Can I run Claude locally?

No. Claude weights are not public. It only runs through Anthropic’s apps, the API and cloud partners.

Can I run DeepSeek V4 on an RTX 4090 or RTX 5090?

Only with a lot of system RAM. V4-Flash at 2-bit needs about 97 GB in total, so a single 24-32 GB GPU must be paired with at least 96-128 GB of RAM, with most experts kept in RAM. On a GPU with 32-64 GB of RAM it will not fit.

Is DeepSeek free?

The chat app at chat.deepseek.com is free to use, and the weights are free under the MIT license. The API is paid, from $0.44 / $1.32 per million tokens for Flash at peak hours.

Is DeepSeek safe to use for private data?

If you use DeepSeek’s own app or API, your prompts are processed on DeepSeek’s servers in China, so check your company’s data rules first. If you self-host the open weights, nothing leaves your machine.

Final Thoughts

In November 2026 the answer is simple: Claude is the stronger model family, especially for agentic coding, computer use and accuracy, and DeepSeek is the value and freedom choice, with MIT weights and prices that are several times lower. DeepSeek’s newest V4.1-Flash-1017 is a big step up over the previous Flash, and if V4.1-Pro arrives with similar gains the gap could narrow, so I will update this comparison when it lands.

If you want to try DeepSeek at home, start with the GGUF quantization guide to pick a file, and read Ollama vs LM Studio vs llama.cpp to choose your runner. For other big open models that fit a 128 GB PC, see my Kimi K3.1 Pro local guide.

Sources

• Artificial Analysis: Claude Opus 5.5 vs DeepSeek V4 Pro 0813
• BenchLM: Claude Opus 5.5 benchmarks
• AI Weekly: Claude Haiku 5.5 launch
• Claude model list and release dates
• Yotta Labs: DeepSeek V4 models, specs and pricing
• Tech Insider: DeepSeek V4.1-Flash-1017
• Unsloth: DeepSeek V4.1 Flash GGUF guide
• Hugging Face: unsloth/DeepSeek-V4-Flash-GGUF
• ComputingForGeeks: DeepSeek V4 Flash local RAM and speed test
• DeepSeek V4.1 Pro release status