Everyone’s asking the same question right now. Which model do I bolt into my personal agent? It’s not 2024 anymore, where the answer was basically “GPT-4 and stop overthinking it.”
The field has split.
You’ve got OpenAI’s GPT-5.5 sitting at the top of the Artificial Analysis Intelligence Index with a score of 60. Anthropic’s Claude Opus 4.7 is right behind at 57, ranked third globally. Then there’s DeepSeek V4 selling near-frontier reasoning for the price of a takeout coffee, Grok 4.5 with a live pipe into everything posted on X, and Google’s Gemini 3.1 Pro, still the one you’d reach for if your agent has to look at screenshots, photos, and messy PDFs and actually understand them.
So which one runs your assistant?
Let me walk through it. Not the hype version. The version you’d want before wiring up your memory files and your tool loops and letting the thing check your inbox at 6 a.m.
The contenders, in one breath
GPT-5.5 is OpenAI’s current flagship and the closest thing to a generalist. It’s strongest at agentic work — terminal sessions, long multi-step jobs, tasks where the model has to plan, act, observe, and correct itself. Claude Opus 4.7 is the careful one: a coder and a close reader that rarely rushes. Gemini 3.1 Pro is multimodal to an almost silly degree. DeepSeek V4 is the budget disruptor with two tiers, Pro and Flash. Grok 4.5 is the live-data wildcard that reads the internet in real time.
Five models. Very different bills.
Cost — this is where it gets wild
Let’s talk actual numbers, because a personal agent runs constantly. It’s not a chatbot you poke twice a day. It’s a background process chewing through tokens while you sleep.
GPT-5.5 charges $5 per million input tokens and $30 per million output. Claude Opus 4.7 is $5 in, $25 out. DeepSeek V4-Pro? $1.74 in and $3.48 out at full price — and it launched with an early discount that dropped input all the way to $0.435. The Flash tier is $0.14 in and $0.28 out.
Read that again. Flash undercuts GPT-5.5 on input by 97%.
Grok 4.5 lands in the middle, marketed as the cheapest “frontier-tier” option. Gemini 3.1 Pro pricing shifts around by tier and context length, but it sits closer to the OpenAI/Anthropic end of the spectrum than the DeepSeek end.
Here’s the thing about agent economics, though. A busy agent doing a 100M-token-input, 20M-token-output day costs roughly $61 on DeepSeek V4-Pro at discount pricing. The same workload on Claude Opus 4.7 is about $1,000. On GPT-5.5 it’s around $1,100. That’s not a rounding error. That’s a decision.
Reasoning and benchmarks
Benchmark tables never tell the whole story, but they tell part of it. On SWE-bench Verified — the “can it actually fix real code” test — DeepSeek V4-Pro scores 80.6% and Claude Opus 4.6 (the version V4 was benchmarked against) scores 80.8%. Basically tied. On LiveCodeBench, DeepSeek edges Claude, 93.5% to 88.8%, and posts a Codeforces rating of 3206.
But Claude still leads on careful knowledge work. Its MMLU-Pro is 89.1% against DeepSeek’s 87.5%. On GPQA Diamond — the brutal science-questions set — GPT-5.4 hit 93.0% to DeepSeek’s 90.1%.
So the gaps are real but small. Two or three points here, four there. In 2024 those gaps were chasms. In 2026 they’re cracks you can step over without looking down.
Context, speed, and reliability
DeepSeek V4 ships a 1M-token context with a 384K max output ceiling, which is genuinely huge for an agent that needs to hold an entire project in its head at once. GPT-5.5 and Claude Opus 4.7 both offer 1M context but cap output at 128K. For most agent loops that’s plenty — but if your assistant summarizes a codebase or a research corpus in one pass, that DeepSeek ceiling matters.
Speed is less dramatic than it used to be. Everything on this list is fast enough for an assistant. The real reliability question is refusal behavior and instruction-following over a long loop. Claude and GPT-5.5 tend to hold a system prompt and a tool protocol together more steadily across dozens of turns. DeepSeek has improved here enormously but can still occasionally wander off a strict JSON schema. That’s an observation, not a law.
Tool use and the things benchmarks miss
Tool use is where the practical differences live. Claude Opus 4.7 got a quiet but meaningful upgrade — high-res image input jumped to 3.75 megapixels with 1:1 pixel coordinates, which is a nerdy way of saying it’s dramatically better at “computer use,” where it has to look at a screen and click in exactly the right spot. GPT-5.5 dominates terminal-based and agentic workflows, the kind where your assistant shells out, runs a command, reads the error, and fixes it without being told. Gemini 3.1 Pro remains the one to beat for mixed-media input. And Grok 4.5’s party trick is still live X data, which matters if your agent should be watching what’s happening now instead of what was in the training set.
A real scenario
Picture a small personal agent. It reads your inbox in the morning, drafts replies, pushes events to your calendar, and occasionally writes a script to scrape something. Maybe sixty agent turns a day.
On DeepSeek V4-Pro, your monthly bill might land around $15 to $40. On Claude Opus 4.7 or GPT-5.5, the same volume runs ten to twenty times that. Whether the extra spend is worth it comes down to what the agent is actually doing. Drafting emails? Any of these handle it fine. Debugging a 4,000-line Python file at 11 p.m.? Claude and GPT-5.5 genuinely pull ahead.
My own setup routes the mundane stuff — inbox triage, summaries, formatting — to a cheaper model, and I only escalate to the expensive ones when something actually breaks. Routing beats loyalty.
The verdict, sort of
Strongest all-rounder, money no object: GPT-5.5. Agent lives mostly in code and careful documents: Claude Opus 4.7. Lives on images and mixed media: Gemini 3.1 Pro. Needs live data: Grok 4.5. Wants 90% of the reasoning for 3% of the price: DeepSeek V4.
There’s no single right answer. That’s the annoying part.
The models caught up to each other this year. The real difference isn’t a leaderboard number anymore — it’s which one fits the specific thing your agent does all day, and what the bill looks like when it’s doing that thing 24/7.
Go check your token logs before you pick. They’ll tell you more than any benchmark will.