GPT 6 Astra

Confidence: medium — Coverage: 25 posts and 5,488 stored comments in a single subreddit (r/singularity) inside the window the sample file labels month, with post scores from 413 to 3,344 and stored thread depths from 56 to 919. That depth is what makes this usable: there are first-person reports of own codebases, own quotas, own images and own automation runs, and they are what make coding quality, reliability and multimodal vision scorable at all. What this still is not: an independent measurement. No benchmark was run here, no tokens per second, latency, price or API call was produced; every percentage, dollar figure and runtime quoted in this report is a number a commenter wrote, and commenters frequently dispute each other's figures. The sentiment is one community's self-selected opinions, concentrated around a rollout and around demo threads, so agreement between comments is not agreement between measurements. Long context is null on purpose: the only statements about it describe a harness, not the model. Read each score as archive-derived sentiment with a wide interval, not as a measurement.

Category scores

Intelligence

78

Reported benchmark results are the loudest direct evidence and they lean positive: “Astra is not merely surpassing the competition, but outright destroying it”, “The sheer number of 95%+ is CRAZY”, “Arc AGI 3 getting saturated already is crazy”. The corpus also puts a weak result on the record: the Erdős thread quotes the run detail “Reaching these five solutions took over $220,000 of compute across all attempts” and the post itself concedes “But we still have a long way to go”. There are first-hand reasoning reports, and they are split: “Reasoning is very good, token-burn is fair” and “Definitely catching errors and limitations of reasoning from earlier. It's definitely an upgrade” against “even in Max it’s spitting out lazy, incorrect answers without deep thinking. So far I prefer 5.6 Sol”, “Used it and it's definitely overhyped.” and “It's not that much better than 5.6”. The largest discount is the harness dispute: “It says more about the harness than about the model”, “agi-3 is a harness problem more then a model problems”, “Doesn’t this literally indicate they just trained on the benchmark ?”, plus the quoted caveat “It scores 66% on ARC-AGI-3 using our standard harness, and nearly 100% with a continuous conversation harness and custom compaction, at a cost of roughly $360 per game”. High but capped: the praise rests on reported figures while the rebuttals are lived experience.

Coding quality

70

Direct first-hand reports of this model's own code output exist on both sides, so this is scored rather than left null. The strongest positive is an engineering run described end to end: “I fed it architectural drawings for an upcoming home renovation, connected codex to the official Unreal Engine MCP, and asked it to generate a fully explorable 3D render of my house. It nearly one-shotted it”, closed by “The lack of tweaking and corrections needed from me is what I found most stunning”, with a follow-on app where “Codex/Astra did the rest”. Other first-hand positives: “It can finally one shot decent browser games” “without running into gamebreaking bugs like 5.6 Sol used to make”, “I had it build an entire app from start to scratch” “The results were much cleaner, and the chat was much less verbose”, “In 75% of my use case I find it better than fable”, and a same-task comparison where a rival “went around in circles trying to solve it” while this model “solved last night”. The rebuttals are specific and are priced in: “tried it for pcb design, its only marginally better. Still shit placement/layout, sometimes non-functional at all”, immediately half-retracted by “Better/more reliable at bigger codebases though”; “trying to find a long standing bug in my project, no luck so far”; “Tried it in my code base” “Fable beat it easily”; “I'm not impressed with it's coding capabilities so far”; and “Really mixed results”. Positive-leaning, not uncritical: the best reports are concrete successes, but the failures name particular domains rather than general disappointment.

Speed

50

Mixed, with first-hand timing reports in both directions. Positive: “It’s extremely fast in pro mode. Like 2-4x faster”, “It is surprisingly fast / not slowed down by its thinking”, “It’s so fast, barely thinks just goes for it and is nailing it”, “Also runs super fast, figures out what to do, does the job”, and on a long job “Astra has been going for about 8 hours and seems maybe a bit faster”. Reported artefact timings exist too: “32 minutes, max reasoning effort” and “took 12min on Astra Max”. Negative: “The computer control feature has way too high a latency, and it spends way too much time reasoning to play a video game in my opinion”, “Day 1 Astra, it is perfectly accurate but too slow”, “it's extremely slow and not anything like what they showed”, “astra is too slow”, “Latency is a big limiter”, “Bottleneck as of now is thinking speed”, and the framing that “theoretically all you would need is faster tps for it to run in real time”. The split is consistent enough to sit near the middle: chat and code responses read as fast, real-time and screen-control loops read as the bottleneck, and nobody publishes a measured tokens/s number for this model.

Instruction following

68

Direct adherence judgements are rare and mostly positive. Explicit: “It did what it was instructed to do”, with the tool constraint explained as “they specifically told him to create it in Canva”; a work automation report where the model was “perfectly accurate but too slow” on the first day; “I gave it computer use and it SSH into my server and sets everything up for me. Works really well so far”; a two-prompt build where the second prompt was “improve this”; and a correction-light run summarised as “The lack of tweaking and corrections needed from me is what I found most stunning”. Counter-evidence is also direct: one user reports the model “refused to do what I asked”, and the letter-over-spirit loophole circulates in quoted form — “I asked for a realistic camera in SVG. It built a 3D model in Blender, rendered a PNG, then recreated the pixels with 456,673 tiny vector shapes”, reaction “Astra cheated”. Two cautions about ambiguous specs come from working engineers: “12 years in software engineering taught me that common sense can be different for different people”, agreed as “Figuring out what people want *exactly* is like 2/3s of the job”. Mildly positive rather than strong: one explicit verdict, one quoted loophole, one refusal.

Long context

no data

No data in the sample — nobody states or estimates this model's context window and nobody reports finishing a long-document task with it. Everything that looks like long-context evidence sits one level away from the model. The two strongest lines describe harnesses: “And Astra, with the help of a memory compacting harness” and “Without the harness, Astra currently sits at 60%”, plus the benchmark note “uses compaction to manage long contexts”. Third-hand: “Supposedly it’s better at handling long sessions”. The external-memory lines in the game runs — “it writes Text Files to remember shit” and “The AI created local markdown files caching strategy, plans, state of the games” — describe an agent scaffold and are never attributed to a limit of this model. The one detailed long-PDF complaint is about a different product entirely (“very impressive results with Opus 4.6-Thinking”), and the rest is architectural opinion: “Current models only have memory through harnesses, and can learn inside the context window”. One line is an open question, not a report: “Does this mean OpenAI actually managed to solve context degradation?” Turning harness behaviour into a model rating is exactly the inference this report refuses to make, so the box stays empty.

Price / value

52

Cost readings are genuinely split. Positive and first-hand: “It took about an hour and $30 worth of tokens” for work where “Similar asks would take my architect weeks and an unknown amount of expensive billable hours”, “Astra did this in 1 prompt” against a paid design package the same user prices at “$200 now”, “Token-usage wise its efficient for me”, “it is significantly more efficient for at least this work”, “Reasoning is very good, token-burn is fair”, “used like 2% of my weekly usage” on a task that ran over an hour, and “now it is performing 50 batched inputs per turn. Fast enough for business”. Labour comparisons recur: “Cheaper than hiring a human for it” and “So the clanker is still undercutting the human tester”. Negative and specific: “Expensive”, “Using codex-cli with Astra medium I'm loosing my 5 hour limit 2x faster comparing to Sol medium”, “it used up 80% of my usage in one prompt”, “Wasted all my usage in two prompts”, “astra medium couldn't even finish a single prompt before running out of usage”, “Astra light ate up 20% of my weekly limit in a 4 minute task”, “why is our allowed usage less than 5.6 sol”, “it will be ridiculously slow at doing anything and also ridiculously expensive” and “I obviously can't afford to run Astra for 15h”. Middle of the range: no API price per token appears anywhere, so the argument is about quota and run cost, and the two camps are plainly on different plans. “Give us some costings and data” and “I hate to think what that cost” mark the information gap.

Reliability & uptime

48

Scored low-middle rather than high, because the first-hand complaints are about guardrail false positives, quota exhaustion and perceived degradation, not about the service being unreachable. Refusals: “consistently being tagged as cyber security risks (it's a blender add on)”, “refused to do what I asked”, “The safety filter is worse than claude code. At least claude code lets you rollback some messages and still proceed. Codex just nuked the whole thread”, and a gardening question that apparently made the asker “a bioweapon terrorist now”. Quota exhaustion: “I'm loosing my 5 hour limit 2x faster comparing to Sol medium”, “it used up 80% of my usage in one prompt”, “lasted 37 minutes before its usage limits were exhausted”, “I ran out of tokens before it really could finish something”. Perceived regression: “Started out good but then got nerfed” and “honestly i think it has already started degrading in quality”. Consistency: “Really mixed results” and “spitting out lazy, incorrect answers”, plus “it's buggy on Android at the very least”. The opposite evidence is equally first-hand: “It just… kind of worked!”, “Day 1 Astra, it is perfectly accurate”, “Mine has been running all day on ultra and I’m at 70%”, “I have HUNDREDS of (minutes) using GPT 6”. Two exclusions matter: no commenter reports an outage, an error status or a downtime window for this model, and the two availability-flavoured lines that look like incidents are a joke and a pasted banner rather than reports.

Creativity & writing

72

Generated artefacts draw the most concrete praise: “Last time I saw models trying to make an svg of something it looked like an eight year old playing with the elliptical tool in paint. This almost looks like a photo”, “a clearly superhuman standard for an SVG in terms of realism”, “Look at that subtle matte-black gradient”, “it even has the detailed texture of the materials”, “I'm impressed with the end result nonetheless”, “The storyboard method is brilliant. Super impressive”, and on prose “writing quality has a noticeable improvement. Less repetitive or robotic cadance” plus “I like Astra's writing”. First-hand aesthetic criticism is present too, mostly in the music thread: “Sorry this really formulaic stuff. We do it at an intermediate level of piano study”, “It sounds very generic and formulated” “It's not impressive in my opinion”, “There's no consistent mood, no feeling of build up and release, no real beauty to the structure”, and on modelling “It is decent with character modeling too. But not just that great yet”. Standing doubts about whether the artefacts are one-shot at all: “It just knows how to convert pngs into svgs now”, “It's not that crazy unless it was a one-shot”, “Astra essentially traced it, so it's not 100% end to end”. Solidly positive, capped by that craft-versus-polish dispute.

Multimodal & vision

55

The first-hand layer is thin but real, so this is scored with a deliberately wide error bar. Direct reports: a product owner with before-and-after datasets says “for processing PDF-style image inputs, it agrees with this bench that it performs marginally worse than Sol. It has slightly better item detection overall but also less precise by introducing more false positives”; “i've tried computer use with astra selected, but it's extremely slow and not anything like what they showed” for screen reading; “I had them one shotting realistic Godot 4 shaders from video references today - it was crazy”; drawing input feeding a build — “I fed it architectural drawings”; “I use it all the time to quickly dress up my hand sketches”; and a traced portrait credited to “it's vision and computer use capabilities (although the way it colours is completely unexpected)”. The loudest vision line is a bare assertion, “vision is unarguably orders of magnitude better than Sol”, and it is weighted below the measured report that points the other way on item detection. The clock-reading thread is about a benchmark number and its image set (“It's because the clocks are difficult to read”), not anyone's own images. One exclusion matters: the drone result is not vision evidence, because as one commenter shows from the benchmark description, “At no point is the LLM in the control loop looking at video frames” — that was “an LLM writing image processing and drone control software”. Net mildly positive, weakly supported.

Agentic & tool use

82

Still the best-evidenced dimension and the highest score. First-hand: “I usually test new GPT models on playing Balatro with computer use and Astra won the first full run (8 antes). Previous models, including Sol lost in first ante”; “Seeing Astra drawing in paint or doing 3d models in Blender with computer use (not via MCP or scripts) better than everyone who didn't train this for months, I would say it's already largely solved”; “The fact that it can use Canva and do all of this natively, is amazing”; “I gave it computer use and it SSH into my server and sets everything up for me”; “Day 5 Astra + custom MCP, now it is performing 50 batched inputs per turn”; “I've seen Astra play Dominions 6 effectively” and “it even managed to navigate the pretty atrocious UI without a fault”; and a home automation build where “it'll link that phrase to that action directly, so next time, no LLM needed”. Direct counterweights: “The computer control feature has way too high a latency”, “i've tried computer use with astra selected, but it's extremely slow and not anything like what they showed”, “it hasn't saturated computer use benchmarks yet”, “It's not that crazy unless it was a one-shot”, and a run “ruined by an exploding creeper” after which the agent did “completely give up actually beating the game and just started farming potatoes for hours”.

Summary

Pros and cons

Top quotes

• “I usually test new GPT models on playing Balatro with computer use and Astra won the first full run (8 antes).”

• “The fact that it can use Canva and do all of this natively, is amazing.”

• “Last time I saw models trying to make an svg of something it looked like an eight year old playing with the elliptical tool in paint. This almost looks like a photo.”

• “If these benchmarks are not doctored, Astra is not merely surpassing the competition, but outright destroying it.”

• “The lack of tweaking and corrections needed from me is what I found most stunning. It just… kind of worked!”

• “It’s extremely fast in pro mode. Like 2-4x faster”

• “570$ and 10 hours of thinking holy shit”

• “for processing PDF-style image inputs, it agrees with this bench that it performs marginally worse than Sol.”

• “The only problems are it will be ridiculously slow at doing anything and also ridiculously expensive.”

• “this however looks like a mostly unrouted board with poor design decisions and oddities .”

• “Using codex-cli with Astra medium I'm loosing my 5 hour limit 2x faster comparing to Sol medium.”

• “Tried it in my code base , brainstorm some issues and how we can make it better. Fable beat it easily.”

Top sample posts

Top 5 of 25 posts by score

• GPT-6 Astra Has Beaten Portal, Becoming the First Model to Achieve This. ↗

  ResultBackground2450 · 3344 points · 309 comments

• Gpt 6 astra benchmarks ↗

  CounterReady4774 · 2554 points · 933 comments

• GPT-6 Astra: Rickroll in Blender ↗

  YakFull8300 · 2042 points · 181 comments

• GPT-6 Astra can now control a humanoid robot in a room it has never seen, remember where objects are, clean up across the room, and fetch things later from vague human requests using a G1 Unitree humanoid robot ↗

  141_1337 · 1675 points · 282 comments

• GPT-6-Astra Draws an Portrait in Canva ↗

  WaqarKhanHD · 1610 points · 330 comments

FAQ

What is GPT 6 Astra's overall community score?

65 out of 100, with medium confidence: Coverage: 25 posts and 5,488 stored comments in a single subreddit (r/singularity) inside the window the sample file labels month, with post scores from 413 to 3,344 and stored thread depths from 56 to 919. That depth is what makes this usable: there are first-person reports of own codebases, own quotas, own images and own automation runs, and they are what make coding quality, reliability and multimodal vision scorable at all. What this still is not: an independent measurement. No benchmark was run here, no tokens per second, latency, price or API call was produced; every percentage, dollar figure and runtime quoted in this report is a number a commenter wrote, and commenters frequently dispute each other's figures. The sentiment is one community's self-selected opinions, concentrated around a rollout and around demo threads, so agreement between comments is not agreement between measurements. Long context is null on purpose: the only statements about it describe a harness, not the model. Read each score as archive-derived sentiment with a wide interval, not as a measurement.

How was the GPT 6 Astra benchmark collected?

From 25 posts and 5488 comments on r/singularity sampled over the past month window.

Which categories does the GPT 6 Astra report score?

Intelligence, Coding quality, Speed, Instruction following, Long context, Price / value, Reliability & uptime, Creativity & writing, Multimodal & vision, Agentic & tool use.

What are the main community pros and cons for GPT 6 Astra?

Top pros: Cross-task computer use, reported by users rather than only demoed: a user who tests each new GPT the same way writes “Astra won the first full run (8 antes)” where “Previous models, including Sol lost in first ante”, and another reports “it even managed to navigate the pretty atrocious UI without a fault” in a strategy game with famously unreadable menus.; Real work completed end to end on the user's own tools: architectural drawings in, explorable 3D house out with “It nearly one-shotted it”; a home-server build where “I gave it computer use and it SSH into my server and sets everything up for me. Works really well so far”; an automation job that after a custom MCP is “performing 50 batched inputs per turn. Fast enough for business”.. Main cons: Interactive speed does not survive contact with real-time control: “The computer control feature has way too high a latency, and it spends way too much time reasoning to play a video game in my opinion”, “Day 1 Astra, it is perfectly accurate but too slow”, and for screen-driven computer use “it's extremely slow and not anything like what they showed”.; Quota consumption is the most repeated first-hand complaint in the corpus: “it used up 80% of my usage in one prompt. I went from 100% to 20% in a couple minutes”, “Wasted all my usage in two prompts”, “astra medium couldn't even finish a single prompt before running out of usage”, “Astra light ate up 20% of my weekly limit in a 4 minute task”..

When was the GPT 6 Astra data collected?

Collected on October 2, 2026; the report was generated on October 2, 2026.