Claude Opus 5 Introduced Near Frontier Intelligence at Half Price
New model leads agentic benchmarks and approaches frontier performance at reduced cost.
Entities: Claude Opus 5, Anthropic, Claude
Anthropic launched Claude Opus 5 via its official account, positioning the model as thoughtful and proactive with intelligence near Fable 5 but at half the price. Independent evaluations from Artificial Analysis confirm it leads the AA-Briefcase agentic knowledge work benchmark and the Intelligence Index, with gains in analytical quality and rubric performance. Community reactions highlight strong results on coding, 3D tasks, mathematical proofs, charts, and document processing. The release includes system card details showing outperformance versus prior Opus versions and competitors on multiple agentic and knowledge metrics.
On several coding and knowledge work evaluations, Opus 5 is the new state-of-the-art:
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19 postsIntroducing Claude Opus 5. It's a thoughtful and proactive model that comes close to the frontier intelligence of Fable 5 at half the price.
Opus 5 is a shocking step up from Fable 5 and 5.6 Sol for spreadsheets Almost couldn’t believe our evals at first A step function increase in intelligence, while as efficient as Sol Feels like another Opus 4.5 moment for knowledge work
@fchollet maybe your criteria will be met not because there is a model for which we can't implement a benchmark they fail on, but because smarter models are launched so fast that we don't have time to
Claude Opus 5 beat Claude Fable 5 on a hard 3D coding test at 3/4 the price. cool experiments by @thehypedotnews - opus 5 vs. fable 5 vs. gpt 5.6 sol vs. kimi k3 opus 5 produced the best-engineered and most visually convincing Three.js results wondering why anyone would even need a Fable 5 outside of some very specific use cases. Opus 5 beats it on most benchmarks while being exactly half the official price. The @thehypedotnews is a 24/7 ai news, analysis and breakdowns, really like their chill-out radio format.
claude opus 5 vs fable 5 vs gpt 5.6 sol vs kimi k3 @AnthropicAI released claude opus 5 today. per the announcement, it's "a thoughtful and proactive model that comes close to the frontier intelligence of claude fable 5 at half the price." key facts from the release: • priced at $5/mtok input, $25/mtok output – unchanged from opus 4.8 • anthropic reports it as state-of-the-art on frontier-bench v0.1 (43.3%), gdpval-aa v2 (1861), arc-agi-3 (30.2%), zapier automationbench (26%) and osworld 2.0 (70.6%) • per the same release, it stays behind mythos 5 on cybersecurity, and behind fable 5 on parts of agentic coding (deepswe 68.8 vs sol's 72.7, frontiercode 53.4 vs fable's 53.5) • runs at max thinking effort by default; a fast mode is offered at ~2.5x speed for 2x the price. our runs used the standard setting our test – 3 prompts, single-file html, @threejs, fully procedural, no assets. each has a small fire button (web audio gunshot + muzzle flash + recoil + ejected brass) and a disassemble toggle that explodes the gun into labelled parts and rebuilds it: 1. 5.56 m4 carbine – collapsible stock, safe·semi·burst lower, quad-rail with r14–r28 panel numbers, aimpoint red-dot, vertical foregrip, folding bipod, a2 flash hider, breaks into 10 parts 2. glock 18c – select-fire machine pistol, ported 18c compensator slots, "glock 18c / austria 9x19" roll-marks, extended 33-round mag, field-strips into slide, ported barrel, recoil spring, frame, mag 3. steyr tmp – ribbed polymer housing, threaded barrel, integral forward vertical foregrip, canted translucent 30-round mag ran on @aimlapi results: - cost #1 gpt 5.6 sol – $1.26 #2 kimi k3 – $3.60 #3 opus 5 – $6.22 #4 fable 5 – $8.12 - tokens #1 gpt 5.6 sol – 36,554 #2 fable 5 – 120,203 #3 opus 5 – 137,093 #4 kimi k3 – 196,596 - lines of code #1 opus 5 – 2,738 #2 gpt 5.6 sol – 2,432 #3 kimi k3 – 1,907 #4 fable 5 – 1,764 - generation time #1 gpt 5.6 sol – 7.8 min #2 fable 5 – 23.5 min #3 opus 5 – 24.8 min #4 kimi k3 – 93.8 min observations: • opus 5 wrote the most code and shipped the best-looking guns – the two go together. 2,738 lines, almost no boilerplate, and it beat fable at 3/4 of fable's price. all 3 models render, all 3 disassemble reliably • it is the only model that respected the black-on-black problem. its studio env is a narrow overhead strip with a comment explaining why ("narrow + long beats big + square here"), and parts separate by roughness, not colour – slide 0.44, ribs 0.52, grip 0.86. that is why the lighting reads like a product shot • the steyr ribbing and m4 proportions are the best in the test. the ribs are real half-buried cylinders merged into one geometry, each catching a single specular line – not a bump map. thin barrel, slim quad-rail, correct 84cm scale, all computed from named stations, not eyeballed • price per 100 shipped lines – gpt sol $0.052, kimi $0.189, opus $0.227, fable $0.460. opus buys frontier-grade detail for half of fable's per-line rate opus 5's code quality: upsides: - it engineers instead of eyeballing. everything is built to real-world scale (m4 = 84cm), the rail teeth sit at the exact 1cm pitch a real picatinny rail uses, and the steyr's ribbing is actual 3d geometry catching a specular line, not a bump texture faking it - it caught a classic three.js trap the others didn't – the bevel on extruded shapes quietly inflates the silhouette, so it pre-corrected every surface constant so decals and ribs sit flush instead of floating or z-fighting downside: - one systemic bug: every left-side engraving renders mirrored. roll-marks, the safe·semi·burst selector, the mag numbers – all reversed. single cause, but it hits all three guns opus 5 is the strongest engineer in this field – it saw traps the others walked into and reasoned about physics constants, real-world scale, and sequences instead of guessing follow @thehypedotnews for 24/7 ai news, analysis and breakdowns
benchmarking opus 5 on slopCodeBench - we are part way into the eval run - will keep posting status updates as we go - so far (no surprise) sonnet 5 slower and more expensive than opus 4.8 and opus 5 Checkpoint 1, problem one: Opus 4.8 - 7 turns Opus 5 - 11 turns Sonnet 5 - 33 turns and counting 😅 more results as they stream in
opus 5 benchmarking on slopcodebench https://x.com/i/broadcasts/1qJVmmPywOXGB
'And when layering defenses -- strong model alignment, combined with prompt injection probes, combined with Auto Mode in Claude Code -- the success rate for prompt injection attacks drops to ~0. This is new and exciting! More about this soon.' It was always inevitable.
Opus 5 is a great model for coding, data analysis, design, biology, knowledge work. More than any of these eval scores, what is most exciting to me is something else: Opus 5 is our least prompt injectable model yet. It is a bit buried in the system card, but across PI evals and red teaming, Opus 5 is very hard to prompt inject successfully. And when layering defenses -- strong model alignment, combined with prompt injection probes, combined with Auto Mode in Claude Code -- the success rate for prompt injection attacks drops to ~0. This is new and exciting! More about this soon. https://www-cdn.anthropic.com/c5fbac3f0b1280a933ebd26d3cb8bb9f5bdeaf48/Claude%20Opus%205%20System%20Card.pdf#page=73