Here’s a question that keeps coming up in 2026. You want to run your own personal AI agent — something that writes your emails, sifts your calendar, maybe ships a few code changes while you sleep. And you don’t want to pay Anthropic or OpenAI prices to do it. So you start looking at open weights. And almost immediately you hit the same fork in the road: DeepSeek V4 or Llama 4.
Both are free to download. Both have huge communities. Both can run on your own box or through a cheap API. But they are not the same animal, and the difference matters more than you’d think once your agent starts doing real work.
The two contenders, briefly
DeepSeek V4 is a Mixture-of-Experts model out of China, released under the MIT license. That last part is a big deal — MIT means you can basically do anything with it, including commercial work, no strings. It comes in two sizes: V4-Pro, a 1.6-trillion-parameter monster with 49 billion active at any moment, and V4-Flash, a leaner 284-billion-parameter sibling. It’s text-only. No image input, no voice. Just raw reasoning and code.
Llama 4 is Meta’s open-weight answer. The family has Scout — small, fast, with a frankly absurd 10-million-token context window — and Maverick, a 400-billion-parameter MoE that’s genuinely multimodal. It can actually look at a screenshot or a photo, which DeepSeek V4 simply can’t. There’s also a “Behemoth” model floating around in preview, but it’s not generally available yet, so we’ll ignore it.
Both are free to grab off Hugging Face. That’s where the easy part ends.
Coding: DeepSeek pulls ahead, hard
If your agent writes code — and honestly, most useful agents do at least a little — this is where the gap shows up. DeepSeek V4-Pro scores 80.6% on SWE-bench Verified, which is an open-weight record as of mid-2026. On LiveCodeBench it posts a 93.5%, and its Codeforces rating sits around 3206. That’s… high. Like, competitive-programmer high.
Llama 4 Maverick is no slouch. It beats GPT-4o on MMLU, HumanEval, and SWE-bench, which is a respectable showing for a free model. But “beats GPT-4o” in 2026 isn’t the flex it was in 2024. Put it next to DeepSeek V4-Pro and Maverick is a clear step behind on the hard coding and agentic benchmarks. Scout is even further back — it’s built for breadth and speed, not for grinding through a gnarly refactor.
So if your agent’s main job is writing and shipping code, DeepSeek V4-Pro is the pick, and it isn’t that close.
Context and multimodal: Llama’s two counterpunches
But here’s where Llama fights back. And it fights back with two things DeepSeek V4 doesn’t offer at all.
First, context. DeepSeek V4 gives you a 1-million-token window, which is already a lot. Llama 4 Scout gives you ten million. That’s the largest context window of any open-weight model in 2026. Ten million tokens means your agent can hold an entire codebase, or months of conversation, or a giant pile of documents, in active memory without summarization tricks. For a personal agent that digests your whole email archive or a big project, that’s a genuine superpower.
Second, vision. Maverick is early-fusion multimodal — it can read a screenshot, a receipt photo, a diagram, a chart. DeepSeek V4 is text-only. If your agent needs to “look” at anything — and a personal assistant that handles your life almost certainly will, from a screenshot of a bill to a photo of a whiteboard — Llama 4 Maverick can do it and DeepSeek V4 can’t.
So the picture inverts. For pure text and code, DeepSeek wins. The moment your agent needs eyes or needs to remember everything, Llama pulls ahead.
Cost: closer than the headlines suggest
DeepSeek’s pricing is famously aggressive. V4-Pro runs $0.435 per million input tokens and $0.87 per million output, with cache-hit input dropping to a near-invisible $0.003625. V4-Flash is $0.14 in and $0.28 out. There’s also a peak/off-peak scheme that kicked in August 16, 2026 — off-peak input drops to $0.66, peak climbs to $1.32.
Llama 4 doesn’t have an official first-party API, so pricing depends on your host. Via Together AI, Scout goes for roughly $0.08–$0.15 per million input, and Maverick lands at $0.27 in and $0.85 out. If you self-host on your own hardware, your “cost” is electricity and VRAM, not per-token fees.
Net result: they’re in the same rough ballpark, with Scout being the cheapest of the bunch for long-context work and DeepSeek V4-Flash being absurdly cheap for straightforward tasks. Neither will make you wince the way a frontier API bill does.
Ecosystem and self-hosting: Llama’s home turf
If you want to actually run the model yourself, Llama 4 has the deepest ecosystem in the game. Ollama, LM Studio, vLLM, llama.cpp — everything supports it, usually on day one. There are thousands of fine-tunes floating around. Quantized versions. Community guides. It’s the default, and that default-ness saves you real time.
DeepSeek V4’s weights are open too, and it runs fine on the same tooling. But the self-hosting story is thinner — fewer fine-tunes, less community muscle around local deployment, and a heavier model at the Pro tier that wants serious hardware. Most people end up calling DeepSeek through its API rather than wrestling it onto a home server.
One more thing worth flagging: licensing. DeepSeek is MIT — do whatever you want, no restrictions. Llama 4 uses Meta’s community license, which has a 700-million-MAU clause. It almost never binds for a personal agent, but it’s a clause. Worth knowing it’s there.
Reliability and speed
Llama 4 Scout is fast. Like, noticeably snappy, which is what you want when your agent is doing ten small tool calls in a row. DeepSeek V4-Pro is a big model and can feel a touch slower on long reasoning chains, though V4-Flash is quick. Reliability-wise, DeepSeek’s API has been solid, with the occasional peak-hour slowdown that the off-peak pricing is clearly trying to smooth out.
A real scenario
Say your agent’s daily routine is: read last night’s emails, draft replies, pull your calendar, then review a pull request. The email and calendar part is text-plus-summarization — DeepSeek V4 handles it beautifully and cheaply. But then it hits the pull request. If there’s a screenshot of a UI change in the PR, DeepSeek is stuck — it literally can’t see it. A Llama 4 Maverick agent can.
Meanwhile, if that same agent has to fix the bug in the PR rather than just review it, DeepSeek V4-Pro runs circles around Maverick on the actual code.
And that tension, right there, is the whole story.
Which for whom
Pick DeepSeek V4-Pro if your agent’s job is mostly code, reasoning-heavy text work, and you want maximum capability per dollar through an API. The MIT license is a nice bonus if you ever want to build something on top of it commercially.
Pick Llama 4 if your agent needs to see images, hold a truly enormous context in memory, or run locally on hardware you control. Scout for cheap long-context tasks, Maverick for the multimodal everything-agent.
Honestly, a lot of people run both. Cheap Flash for the boring text chores, Llama Scout for the huge-context memory work, and one strong reasoning model where it counts. But if you’re forcing me to pick a single default for a personal agent in 2026 — I’d go DeepSeek V4 for pure code, and flip to Llama 4 Maverick the second your assistant needs eyes. I keep both configured myself, which probably tells you more than any benchmark table would.