A benchmark-grounded comparison of OpenAI Astra and DeepSeek models that identifies exactly when the 40x price difference is and is not justified.
Adapted from @alex_verem# frontier intelligence is actually cheap OpenAI charges $50 per million output tokens for GPT-6 Astra. DeepSeek charges $1.20 for DeepSeek-V4.1-Flash, and on the one coding benchmark where both companies published a score for the same version, the two models are 0.1 points apart. More than 40 times the price for a tenth of a point. That's the whole question. If you're paying for Astra, what are you paying for that the open models can't do? For most work, the answer is nothing you'd notice. The exceptions are real, and they're specific enough to name. Every number in this piece meets one test. The model's developer and OpenAI both published a score on the same benchmark version. I took them from the developers' own model cards, READMEs, and launch posts. ## Start with what your work looks like Before any number, sort your own work into one of two kinds. The first kind is a task you describe and the model finishes. Fix this bug, answer this question from these sources, draft this, refactor this module. The task has a defined end and you can check the result. This is where open weights caught up. The second kind is a computer you hand over. The model opens applications, reads screens, clicks, types, recovers from whatever it finds, and runs for hours without you. This is where OpenAI publishes numbers and no open weight developer publishes a comparable one. I sorted everything below by that line. If you know which side your work sits on, skip to your section. ## Coding Coding is the job most people are deciding about, and it splits in two. For scoped work, the numbers are the same. DeepSWE v1.1 is a benchmark of real repository tasks with a defined end state. OpenAI reports 74.1 for Astra. DeepSeek reports 74.2 for V4.1-Flash. A 0.1-point difference on a 100-point scale doesn't separate the two, so read it as a tie. The one caveat is that OpenAI doesn't say which agent harness produced its number, and DeepSeek does (mini-SWE-agent), so nobody outside OpenAI can line the two runs up exactly. That makes it a tie you can't verify, and a reason not to call it a win for either side. For your work. If your coding work is tickets, bug fixes, tests, refactors, and reviews. The kind of thing you'd hand a contractor with a clear brief. You're paying 40 times more per token for a difference the benchmark can't detect. For long unattended sessions, the numbers are not the same. Terminal-Bench 4.0 gives an agent a real terminal and a hard multi-step task, and scores whether it finishes. OpenAI reports 57.9 for Astra. DeepSeek reports 31.2 for V4.1-Flash, published September 10, 2026, the first open-weight score on the 4.0 version. The gap is 26.7 points. On a benchmark where the best model finishes a little over half the tasks, that's the difference between an agent you can leave alone and one you have to supervise. For your work. If it's a model driving its own environment for hours. It's provisioning, migrating, debugging across a system with nobody checking each step. Astra finishes tasks the open models don't, and the $50 is buying something. The pick here is DeepSeek-V4.1-Flash for scoped coding, if you're comfortable with a model whose weights went public on September 10, 2026. Kimi K3 (67.5 on DeepSWE v1.1, $15 output) and GLM-5.3 (66.9, $4.40) are the fallbacks with a longer public record, both within 7 points of Astra. If price matters more than the last few points, GLM-5.3-Flash scores 63.4 on the same benchmark at $0.50 output, which is 10.7 points behind Astra for one hundredth of the price. ## Research and knowledge work BrowseComp tests whether a model can find hard-to-locate facts by searching the web. OpenAI reports 91.5 for Astra. Moonshot AI reports 91.2 for Kimi K3. Both companies cite the same reference score for the same third model, GPT-5.6 Sol, at 90.4, which is the best sign two labs measured the same thing. On GPQA Diamond, graduate-level science questions, OpenAI reports 96.0 for Astra and Moonshot AI reports 93.5 for Kimi K3, a 2.5-point gap on a test where both models answer more than nine questions in ten. For your work. Fact-finding, literature review, research synthesis, answering questions from long documents, drafting from sources. The gap is inside the noise, and Kimi K3's output price is $15 per million against $50, less than a third. If you'd rather not depend on one vendor, Qwen3.8-2.4T-A95B sits 3.4 points behind Astra on the same science test. The call? No case for Astra here. ## Computer use and new environments This is where the case for Astra is strongest, and where the evidence for the open models is absent. OpenAI reports 72.6 on OSWorld 2.0, a benchmark of a model operating a real desktop, and 99.9 on ARC-AGI-3, where the model has to learn the rules of an environment it has never seen and act efficiently inside it. OpenAI's post quotes Greg Kamradt of the ARC Prize Foundation saying Astra passed the human action-efficiency baseline on 96 percent of levels. None of the open-weight developers publish an ARC-AGI-3 score. On OSWorld, Moonshot AI publishes 58.3 for Kimi K3 on a different cut of the benchmark than OpenAI used, so the two numbers can't sit side by side. OpenAI also reports 92.7 on ScreenSpot-Pro, a test of locating interface elements on a screen, and no open-weight developer publishes that one either. For your work. If you're paying Astra to operate software on your behalf, or to handle situations it hasn't seen before, there's no published evidence an open model does that job. There's also no published evidence it doesn't, because absence isn't a score. The safe reading is that this is the one workload where the $50 buys something no open model has shown it can match, and that stays true until a developer publishes a same-version number. The call. Stay on Astra, or run your own test on your own tasks before moving. Don't assume the coding result carries over. It's a different skill. ## What switching means in practice Open weights give you two routes, and it's worth being honest about which one you'll take. Route one is downloading the weights. Every model named above except Qwen3.8-27B runs to hundreds of billions of parameters, and none of the six model cards say what hardware it needs; each points you to a vLLM or SGLang recipe instead. The one that fits a single GPU, Qwen3.8-27B, is 32 points behind Astra on DeepSWE v1.1, which takes it out of this comparison. Route two is the developer's own API. DeepSeek, Moonshot AI and Z.ai all sell the models above at the prices quoted. It's 3 to 40 times cheaper on output, and it's renting all the same. Your prompts go to their servers, and their terms apply. Licenses won't be the reason you choose. All six are MIT or an MIT-style license with clauses that only apply above $20 million in revenue. So here is what "own your stack" means for most readers. Use the API first. Move to your own hardware when your token volume justifies a server. And keep the option to do that at any time, because the weights exist and Astra's don't. That option is the thing you own, and it's the reason the comparison is worth making even if you never download a file. ## The decision Scoped coding, research, writing, anything with a defined end you can check. Switch. Keep an Astra key for spot checks if you want one. Long autonomous terminal work. Stay on Astra. The 26.7-point gap is real, and no open-weight developer has published a number that closes it. Computer use and unfamiliar environments. Stay, or test on your own tasks first. There's no open-weight number to trust either way. Astra shipped on September 3. The open model that tied it on coding shipped on September 10. The gaps in this article move by the week, so check the date on every score before you act on it. If you found this useful, check out my newsletter below I share one AI superpower every week Subscribe, it's free https://linktr.ee/alex_prompter
