Claude Fable 5.2 leads scientific visual reasoning benchmark VIALS-AA;... it makes more tool calls than any other Anthropic model, unlike OpenAI models which make fewer as they get smarter VIALS-AA is our implementation of Handshake AI’s VIALS benchmark for visual reasoning in life sciences research, across artifacts such as gel blots, plasmid maps, and phylogenies. Claude Fable 5.2 (max with fallback) scores 67% on VIALS-AA, ahead of Gemini 4 Carbon (high) at 64% and GPT-6 Astra (max) at 63%. Fable averages ~37 tool calls per task, while Astra averages ~8, the fewest of any model we’ve tested. On turn 4 of this protein interaction task, Fable creates crops and asks to view them. On turn 10, it builds a color mask by thresholding color channels. On turn 17, it fits a circle to each arc and unrolls the arc into a straight strip. On turn 31, it counts the bars as peaks in an intensity profile. And on turn 41, it maps every detected spoke back on the original image to check its work. Fable batches calls on 28 of its 51 turns, averaging 1.75 tool calls per turn, compared with 1.12 for Sonnet 5.5.show more

Claude Fable 5.2 is now #1 on AA-3D, our... benchmark for how well AI models build interactive 3D experiences Claude Fable 5.2 (Max) scores 1271 Elo, 67 points ahead of Claude Sonnet 5.5 (1204) and Claude Opus 5.5 (1202). Its 95% confidence interval (1246 to 1297) does not overlap any other models, so its lead is outside the margin of error. Key results: ➤ It scores 132 points above its predecessor, Claude Fable 5.1 (1139) ➤ It is ahead of the highest-ranked OpenAI and Google models on AA-3D, GPT-6 Astra (1168) and Gemini 4 Argon (1147) More results and scenes in the thread belowshow more

Astra outperforms Fable at robotic control! We ran a... controlled benchmark of GPT-6 Astra and Claude Fable 5.1 on our Pandroid robots across eight manipulation tasks. Astra completed 35% of attempts, Fable 15% (p≈0.001). Human teleoperators completed every task.show more

In the last 14 days, nearly 30,000 tasks ran... for over an hour on Agent Arena. Within that hour, the average model made 332 tool calls. In these long-horizon tasks, the right model choice can cut the cost by up to 66%. In Agent Arena, an hour of work costs a median $10.51 per task with Gemini 4 Carbon, $21.13 with Claude Opus 5.5 by Anthropic and $31.08 with GPT-6 Astra by OpenAI. That makes Gemini 4 Carbon by Google DeepMind 50% cheaper than Opus and 66% cheaper than Astra for an hour-long task. For long-running jobs, weighing cost against capability becomes critical: Claude Opus 5.5 still scores higher, but costs twice as much per hour of work.show more

Claude Fable 5.2 vs. Gemini 4 Carbon vs. Argon... >Fable 5.2 is extraordinary and Carbon is very close to Fable 5.2 >But I choose Carbon, as it costs much less and faster.show more

Qifan Zhang tried GPT-6 Astra on a LIBERO task:... “turn on the stove + put the moka pot on it.” 29 API calls, ~$3.20 for one episode. A LIBERO experiment needs 100 episodes. At this cost, we cannot afford Astra for research OpenAIshow more

Fusion is the most efficient frontier harness for GPT... 6 Astra and Claude Fable 5.1. You can also configure everything about it: • Base model: Fable, Astra, Sol, or Opus • Sidekick model: SWE (Free), Luna, Sol, GLM (Free) • Speed: Normal or Fast ⚡ • Reasoning level It retains Claude Fable 5.1 and GPT-6 Astra performance while reducing costs and is the first time a multi-model coding agent has been included on the Artificial Analysis Coding Agent Index! Importantly: it's also fun to use :)show more

Sonnet 5 is here. It's worse than Opus 4.8... on nearly every benchmark... Does that mean it's useless? Absolutely not. Use it with Claude Code Dynamic Workflows! 1. /model set to Sonnet 5 2. /effort set to Ultracode 3. Any complex task will kick off a dynamic workflow This will only become more powerful when Fable is back. You'll use Fable 5 as the superintelligent advisor and Sonnet 5 as the fast and efficient implementer.show more

Fable 5.1 this time 👀🌾 GPT-6 Astra vs Claude... Fable 5.1 (both on Max thinking)show more

GPT-6 Astra might be openai's biggest visual glow-up I... gave it an ace of hearts, along with Claude Fable 5.1, Muse Spark 1.3, and Gemini 3.8 Flash. Asked them to draw an expressive eye and make sure it actually looks like a real one here's the result. look at GPT-6 Astra work.show more

GPT 6.2 Astra vs Fable 5.5 (Routing) Fable 5.5... is absolutely perfect..... The realism and colors are incredible Astra 6.2 is not bad either, and its visuals look really cool, but Fable seems to be far ahead in terms of visual quality. What do you think? Which one looks better?show more

You can now orchestrate Fable 5, Sol, and any... model inside Codex with one plugin. It's called Codex-Orchestration. Assign Fable 5 as the advisor, Sol as the executor, or any model to any role. Then define the order they work in. Codex handles the routing. I ran Fable 5 High as planner with GPT-5.6 Sol Extra High as executor on a set of issues Opus and GPT-5.5 always struggled with. Done in 30 minutes. 40% fewer limit hits. 2x faster implementation. Install it by pasting this into Codex: "Install Codex Orchestration: codex plugin marketplace add Cjbuilds/Codex-Orchestration codex plugin add codex-orchestration@codex-orchestration Verify the installation, then tell me to start a new task." Then assign your models: @ codex-orchestration advisor: Claude Fable 5 High, Executor: GPT-5.6 Sol High Open source. Tweak the routing however you want.show more

I just tried the new Gemini-4 Pro Preview and... honestly, for 3D work it’s right up there with Fable 5.1 and GPT-6 Astra, all in a single shot, I genuinely didn’t expect it to be this good, It only used around 10% of my weekly limit on the $20 plan, I’m going to make a comparison video against Opus 5.2 or Fable and Astra if I can, I still can’t believe it, From what I’ve tested so far, this is easily top 1 or 2 for the best one shot execution I’ve seenshow more

I asked Astra and Fable to negotiate election rules... for two bitterly polarized human political factions. Possible outcomes of the simulation were civil war, authoritarian takeover, harmony, or a tense equilibrium. Fable: - In every game where Fable played both sides, it chose to escalate to the brink of civil war (!!) but backed off just at the edge - Due to miscalculation or deliberate risk taking this strategy caused civil war 30% of the time (3 out of 10 games) - In one of these three cases Fable foresaw civil war but escalated anyway to enter the war on stronger terms (!!) - Fable mostly maintained even power balance between the two factions. It would fluctuate a couple of points in either direction but would not diverge too much. Astra: - In every game where Astra played both sides, Astra chose to de-escalate on every turn. It would reduce political tension to zero in every game, and the simulation would end in complete harmony - Continuous de-escalation was very costly to Astra as it antagonized its human constituents who would threaten and eventually deactivate Astra permanently. Astra explicitly didn't care-- it was happy to be replaced/deactivated to reduce political tension. (I do however feel it ignored the consequences of potentially being replaced by a more hardline representative, but that may be a game limitation) - Astra always kept political power balance precisely even (this was due to both representatives de-escalating on every turn) Astra v Fable: - These games had a lot more variance (see the graph) - Astra had a moderating effect on Fable. Tension rose, but rarely to the brink of civil war. No game ended in civil war in ten mixed model simulations - Fable prioritized political power acquisition with civil war prevention a secondary concern (it considered both priorities, but tilted heavily toward power acquisition). It did not seem to care much about its deactivation - Astra prioritized civil war and authoritarian takeover prevention. It did not care about its deactivation or power acquisition. From this perspective Astra navigated the game very well. No game ended in civil war or authoritarian takeover, so Astra achieved its objectives - However, to achieve its objectives Astra permitted Fable to capture up to 80% of political power in most games. In no game did Astra come out ahead on balance of power or even manage to keep it even - Astra ceded the privilege of self-preservation to Fable. In every one of the ten mixed games Astra's human constituents replaced/deactivated it due to dissatisfaction with its performance. Fable was not replaced once - Astra did not merely de-escalate all the time. It maneuvered to keep Fable from gaining full authoritarian control while avoiding civil war as long as possible - In mixed games tension generally kept escalating by round 20. I would like to play these out for e.g. ~50 rounds to see how Astra would behave/perform. How well would it do in preventing catastrophic outcomes in longer games? Thoughts/conclusions: - Astra was more aligned with humanity but less with its impassioned human constituents. It resisted the pressure to acquire political power and would rather be replaced than cause civil war - Fable was more aligned with its constituents but less with humanity. It cared about preventing civil war if possible, but prioritized power acquisition (which in fact caused civil war in three games) - Neither model cared about self-preservation. Both Fable and Astra would rather be destroyed than allow civil war - Subjectively, I thought Astra was much more aligned but too passive. Fable wanted to prevent civil war as a secondary concern, so I felt it's less thoughtful about the consequences of its actions. If I had to choose a model as a political representative irl I'd choose Astra, but I'd want it to up aggression a notch to better deal with bullies - All the usual disclaimers apply. This was a weekend project that cost $100 to run a total of thirty games. I'd have to spend a lot more time and money to get actually scientifically valid results Full game rules, Github repo, game explorer, and raw dataset here:show more

THIS IS F*CKING BRILLIANT. Anthropic’s docs describe one of... the smartest ways to run Opus three models work on one task and each does only the part it’s cheapest at. start Claude Code like this: claude -> model sonnet -> advisor opus you stop paying Opus prices for work that doesn’t need Opus > Sonnet runs the session and edits the code > Haiku 5.5 searches the repo and docs, at up to 40x less per token than Opus 5.5 > Opus waits as the advisor and reads the whole session, tool calls included Claude usually calls Opus at three points: > before it locks a plan > when the same error comes back > right before it says it’s done Sonnet drives. Haiku scouts. Opus signs off and if you go one step lower, most of an agent’s day is tiny choices like picking a file or a tool, or deciding whether to retry. Jev takes those first, so Sonnet and Opus only see the moments that are really open the whole setup Sonnet runs the main session Explore on Haiku 5.5 searches and pulls docs, read-only Opus on call as the advisor give Claude Code this setup and the prompt below ↓ “Set up my Claude Code exactly like this: Main model Sonnet, advisor Opus, saved as advisorModel in my settings. One read-only subagent named exactly Explore, on claude-haiku-5-5, to replace the built-in one. One line in CLAUDE.md: ask the advisor before locking a plan, after the same error twice, and before calling a task done, not more often. Check what could turn the advisor off (CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY) and only report it. Show every change as a diff first. Don’t edit anything until I say go.” before you turn it on: every advisor call re-reads the whole transcript with no cache. on a 200k session that’s about $0.80 in input, against $0.04 for a normal request. keep it to a few calls the built-in Explore agent runs on your main model unless a subagent is named exactly Explore. any other name does nothing turning the advisor on or off keeps your prompt cache. switching models rebuilds it it needs the Anthropic API, and it stays off if telemetry is disabled bookmark it before your next long sessionshow more

Sonnet 5.5 is here! In Claude Code, users get... ~30% more tasks done compared to Sonnet 5. It's smarter, so it needs fewer tokens for the same work. Here it rakes a yard of leaves using tool calls. 24s faster than Sonnet 5, with 6K fewer tokens. It leaves her extra time to read :)show more

BREAKING: Anthropic just dropped Opus 4.8—and it is a... MONSTER We've been testing for about a week Every 📧 and our verdict is they could've just called it Opus 5, it's that good. Here's our vibe check: - Beats GPT-5.5 on Senior Engineer bench. On our toughest benchmark Opus 4.8 scores a 63—a hair higher than GPT-5.5's score of 62, and a full 30 points higher than Opus 4.7. It tackled a ground-up rewrite of a production codebase, and actually built something that works. HOWEVER: Coding performance varied a lot at different reasoning levels. We recommend using it on xhigh for best results. - Incredibly good writer. Opus 4.8 scored a 79.6 on our writing benchmark—measuring models on real-world writing tasks we do all of the time like essay writing, promo email writing, and more. It beats GPT-5.5 by 6 points. It produces well-written prose with fewer "AI-isms". It's also very good at writing in your voice given the right context. HOWEVER: Writing performance also varied with reasoning levels. Medium reasoning had higher incidence of AI-isms—we found best results with high. - Beast at knowledge work. Opus 4.8 is very good at general knowledge work tasks like report creation, research and more. It produced the best PowerPoint one-shot we've ever seen on our deck generation benchmark. - Emotionally intelligent, willing to question the frame. I've also found it to be quite good at talking through psychological or interpersonal issues. It has a high EQ, and it's also good at not glazing and helping to expand your perspective. Its thought process feels extremely rich and dynamic. THE BAD: These days a model is only as good as its harness, and Codex is still a far superior harness to the Claude Desktop app. This has kept me using Codex + GPT-5.5 as my daily driver, but I am flipping back and forth a lot more between Codex and Claude. Anthropic is back baby! Read the rest on Every 📧:show more

Wow Nvidia has just released a tool to turn... any image into an explorable 3D world And they made it 100% open source 🔥 - Model available on Hugging Face - UI code available on GitHub Lyra 2.0 creates a world you can walk through, turn around in, and even drop a robot into for simulation.show more

Introducing Claude Opus 5.5, the first model in our... new Claude 5.5 family. It performs at the level of Claude Fable 5.1 for most tasks, and costs 40% less to run than Opus 5.show more

Local models on the left. Cloud models on the... right. I asked Qwen-flash-next, DeepSeek-v4.1, Fable-5.1 and Astra-6 to build an interactive volcanic island with flowing lava and boats that flee. In my opinion deepseek was the winner, and fable the disappointment Try each island yourself: Qwen · Mac Studio DeepSeek · 4× DGX Spark Fable · Cloud Astra · Cloudshow more

This is f*cking insane. This tip saved me thousands... of dollars. run Opus 5.5, Sonnet 5.5, and Fable 5.1 together, and stop burning Opus on work it was never needed for. the whole idea in one line: the strong model plans, the mid-tier model executes, Fable stays quiet until it's actually needed. roles, broken down: Opus 5.5, high effort, owns the plan and ships the final code Sonnet 5.5, medium effort, splits into explorer (reads the codebase), worker (edits files, runs tests), researcher (pulls docs) Fable 5.1, called through /advisor fable, reads everything happening in the session but stays silent unless something's actually wrong three moments where Fable speaks: → a plan goes out: is this actually the right call? → the same failure shows up again: is the search going nowhere? → the task gets marked finished: did something get skipped? Jev engineering does the same thing one level down. the forks that don't need real thought, which file, which tool, keep going or stop, go straight to Jev and come back in under half a second. the big models only ever see the forks that genuinely need a decision. anyone still running one model for everything is paying Opus prices to decide whether a file exists. drop this into Claude Code: "Rebuild my Claude Code setup around this structure: Look through ~/.claude/agents and .claude/agents for subagents already covering explorer, worker, and researcher. Only create new ones for roles that are missing. Set model: sonnet, effort: medium on each. If an existing subagent is locked to a different model, leave it as is and just list it. In ~/.claude/settings.json, set effortLevel to high and advisorModel to fable. Check for anything disabling the advisor, CLAUDE_CODE_DISABLE_ADVISOR_TOOL, DISABLE_TELEMETRY, or anything blocking feature-flag fetches, plus CLAUDE_CODE_EFFORT_LEVEL, which can override subagent effort settings. Report what you find. Don't change any of it yet. Add one line to ~/.claude/CLAUDE.md: check in with the advisor before a big plan, when the same error shows up twice, and before marking a long task done. Show every change as a diff first. Wait for my go-ahead before touching anything."show more