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Text of the page (random words):
kens for the same text so the sticker price undersells what you ll actually pay after a week of using it as my daily driver in claude code my take is that sonnet 5 is the model most people should be running now and opus 4 8 has shrunk to a specialist tool you reach for on the genuinely hard problems what claude sonnet 5 actually is anthropic ships models in three sizes haiku for cheap and fast sonnet for the middle opus for the frontier sonnet has always been the workhorse the one that runs when you don t think about which model you re using sonnet 5 is the fifth generation of that workhorse and the pitch is that the middle tier has crept close enough to the top tier that the top tier stops being the obvious choice it landed as the default for free and pro on claude ai the day it launched with access for max team and enterprise plus the api id claude sonnet 5 on the claude platform and inside claude code that default placement counts for more than the benchmarks most claude traffic never picks a model on purpose whatever anthropic sets as the default writes most of the code drafts most of the emails and shapes what people think claude is that makes sonnet 5 the new baseline for almost everyone who touches claude whether they know the version number or not i flipped my claude code default to it on launch morning and left it there for a week running it against a go service with a messy migration path a next js dashboard and a pile of one off scripts this was the everyday work sitting in my terminal the kind i d normally split between claude and codex what stuck with me is that it finishes things multi step tasks that used to stall halfway and wait for me to nudge them now run to completion on their own and that single behavioral change is worth more than any single digit benchmark bump the benchmark breakdown here are the numbers anthropic and early testers published lined up against the two models sonnet 5 is really being measured against its own predecessor and the flagship one rung up benchmark sonnet 5 sonnet 4 6 opus 4 8 swe bench pro agentic coding 63 2 58 1 69 2 terminal bench 2 1 80 4 74 6 osworld verified computer use 81 2 78 5 83 4 browsecomp agentic search 84 7 gdpval aa v2 knowledge work elo 1 618 1 615 frontiercode 38 8 15 1 two rows stand out on swe bench pro the 5 1 point jump from sonnet 4 6 to sonnet 5 58 1 to 63 2 is a real generational gain on a benchmark where model families usually move a couple of points per release frontiercode more than doubled in reported results from 15 1 to 38 8 since frontiercode targets the hard long horizon coding problems that break most models a 2 5 jump there is the clearest signal that sonnet 5 got structurally better at sustained agentic work rather than merely more polished the terminal bench result is the one that made me raise an eyebrow a mid tier model beating the flagship on any benchmark is unusual and terminal bench 2 1 measures exactly the kind of shell driving command running output reading loop that agentic coding lives in sonnet 5 s 80 4 against opus 4 8 s 74 6 lines up with what i felt using it for the day to day loop of edit run tests read the failure fix rerun it is genuinely excellent and the extra brains in opus don t help much on that kind of task where opus still wins is swe bench pro and osworld both by a handful of points those gaps are real but small and they narrow further once you weigh the cost difference the gdpval knowledge work elo is essentially a tie 1 618 vs 1 615 which tells you the reasoning and writing quality of the two models is now hard to tell apart on ordinary tasks a week of real use benchmarks tell you a model s ceiling a week of use tells you its floor how it behaves on the boring 90 of tasks that never make it into a benchmark suite the best thing about sonnet 5 is the self verification loop on my go service i asked it to migrate a set of handlers from a hand rolled router to chi and update the tests sonnet 4 6 would have written the change and stopped leaving me to run the suite and paste back the failures sonnet 5 wrote the change ran the tests itself saw two of them fail on a middleware ordering bug fixed the ordering and reran until green all without me typing anything between the first prompt and the finished diff it did this consistently over the week not once as a lucky demo early coverage reports the same pattern the model runs tests reads the failures patches and reruns without being told to that is the behavior that turns ai that writes code into ai that finishes a task the second thing i noticed is that it s honest about uncertainty more often on an ambiguous refactor where i hadn t specified whether to keep backward compatibility it stopped and asked instead of guessing and plowing ahead that s a small thing that saves a lot of wasted diffs it still has clear failure modes on a gnarly next js server component bug involving a hydration mismatch sonnet 5 went in circles twice it proposed the same wrong fix i rejected it and it came back with a slight variation of the same wrong fix i switched that one task to opus 4 8 and it found the actual cause a date being rendered without a stable server client format in one pass that is the shape of the current gap on the genuinely hard ambiguous problems that need real backtracking opus still pulls ahead on everything else you won t notice a difference the tokenizer tax the pricing page leaves out one detail sonnet 5 ships with an updated tokenizer and anthropic s own announcement says it produces roughly 1 0 to 1 35 more tokens for the same input text than sonnet 4 6 did that multiplier is not uniform plain english lands near the low end while code structured data and non english text push toward the high end what that means in practice the 2 10 intro price is genuinely cheap but your token counts go up so the effective cost per request rises more than the sticker drop suggests if you re comparing sonnet 5 s 3 15 standard price against sonnet 4 6 s 3 15 they look identical on paper but your bill won t match because the same task now consumes more tokens i watched this happen in claude code s usage meter over the week comparable tasks showed noticeably higher token counts than i was used to under 4 6 this doesn t make sonnet 5 expensive it s still dramatically cheaper than opus 4 8 but it does mean you should run your own numbers before assuming a migration is a pure cost win if you have a high volume production pipeline with tight per task budgets measure the real token consumtion on your actual prompts rather than trusting the price per million headline model input 1m output 1m notes sonnet 5 intro through aug 31 2 10 new tokenizer 0 35 tokens sonnet 5 standard from sep 1 3 15 same sticker as 4 6 higher token use sonnet 4 6 3 15 older cheaper per token tokenizer opus 4 8 5 25 flagship reach for the hard tasks when to use sonnet 5 vs opus 4 8 after a week my routing rule is simple send everything to sonnet 5 by default reach for opus 4 8 only when a task is hard enough that a wrong answer is expensive a subtle concurrency bug an ambiguous spec that needs careful interpretation a refactor where backtracking pays off keep haiku 4 5 for the high volume latency sensitive calls where you d never have used a big model anyway that s a real shift from three months ago when opus was my default for anything that mattered and sonnet was the good enough save money option now the ordering has flipped sonnet 5 is the default that s good enough for almost everything and opus is the specialist you escalate to with fable 5 sitting above it for the longest autonomous runs the gdpval tie and the terminal bench win explain why on the work that fills most of a day the two models produce output i can t reliably tell apart so paying 2 5 more for opus stops making sense if you re weighing this against non anthropic options the picture is more crowded than it was glm 5 2 undercuts everyone on price with open weights and lands close on coding benchmarks and gpt 5 5 trades blows on general tasks sonnet 5 s edge over both is the agentic follow through the finish the task behavior which is exactly the thing that doesn t show up cleanly in a single benchmark score for a broader field our gpt vs claude vs gemini coding comparison still holds up on the general shape of the tradeoffs even if the specific version numbers have moved how to switch to sonnet 5 if you re on claude ai free or pro you re already using it it s the default for everything else you point at the model id via the api with the python sdk from the anthropic console import anthropic client anthropic anthropic reads anthropic_api_key message client messages create model claude sonnet 5 max_tokens 4096 messages role user content refactor this go http handler to use chi router and update the table driven tests print message content 0 text enter fullscreen mode exit fullscreen mode in claude code set it as the session default so every agent turn uses it one off for the current session claude model claude sonnet 5 or make it sticky in your shell profile export anthropic_model claude sonnet 5 enter fullscreen mode exit fullscreen mode if you drive claude through a coding ide the same model id works i run it as the default in cursor for ordinary edits and only flip to opus when a task fights back one thing to test before you migrate a production prompt the new tokenizer can change how structured inputs get parsed so if you have prompts that depend on exact output formatting or tight tool call schemas run them through once and check the edges rather than assuming a drop in swap here s a quick way to sanity check the token difference on your own text before and after switching using the count tokens endpoint import anthropic client anthropic anthropic resp client messages count_tokens model claude sonnet 5 messages role user content open prompt txt read print sonnet 5 input tokens resp input_tokens enter fullscreen mode exit fullscreen mode run that against a representative prompt compare it to what the same text cost you under sonnet 4 6 and you ll know your real multiplier instead of guessing at the 1 0 1 35 range who should use it who should skip good fit anyone already on claude for coding sonnet 5 is a straight upgrade over 4 6 in every category and during the intro window it s cheaper too teams running agentic workflows claude code cursor custom harnesses where the self verification loop saves human turns cost conscious builders who were paying for opus out of habit most of that work drops to sonnet 5 with no quality loss you ll notice think twice high volume production pipelines with razor thin margins measure the tokenizer tax on your real traffic before you assume the migration saves money anyone whose prompts depend on exact tokenization or output formatting test the edges first work that lives at the hard end of the difficulty curve deep debugging ambiguous specs tasks that need backtracking keep opus 4 8 in reach for those and see our claude code max vs pro breakdown if you re deciding how much anthropic compute to buy faq how much does claude sonnet 5 cost introductory api pricing is 2 per million input tokens and 10 per million output tokens through august 31 2026 moving to 3 15 after that on claude ai it s the default model on the free and pro plans at no extra cost factor in the new tokenizer which uses 1 0 1 35 more tokens for the same text when you estimate real spend is claude sonnet 5 good for coding yes it s the strongest mid tier coding model anthropic has shipped it scores 63 2 on swe bench pro up from sonnet 4 6 s 58 1 and beats opus 4 8 on terminal bench 2 1 the standout in daily use is agentic follow through it runs tests reads failures and fixes its own code without being told to claude sonnet 5 vs opus 4 8 which should i use use sonnet 5 as your default for almost everything it s close to opus 4 8 on most benchmarks wins on terminal bench and costs less than half as much reserve opus 4 8 for the hardest tasks subtle bugs ambiguous requirements anything that needs real backtracking where its extra reasoning still pulls ahead what is claude sonnet 5 s context window sonnet 5 supports a 1 million token context window matching the top of anthropic s lineup that s enough to hold a mid size codebase long agent trajectories or large documents in a single session when was claude sonnet 5 released anthropic released claude sonnet 5 on june 30 2026 and made it the default model on the free and pro plans the same day sources introducing claude sonnet 5 anthropic official announcement pricing availability and the tokenizer change claude models overview claude platform docs model ids context windows and standard pricing claude sonnet 5 vs sonnet 4 6 vs opus 4 8 marktechpost benchmark table and cost performance analysis claude sonnet 5 pricing the hidden costs behind the cost neutral launch finout breakdown of the tokenizer s effect on real spend bottom line claude sonnet 5 is the clearest sign yet that the interesting action in ai coding has moved from the frontier tier to the tier just below it it nearly matches opus 4 8 on the benchmarks that matter beats it outright on the terminal driving loop that agentic coding actually runs on and does it at less than half the price after a week of leaving it as my claude code default the only tasks i sent back to opus were the two or three genuinely hard bugs a week throws at you the tokenizer tax is the one thing to watch sonnet 5 is cheaper than opus and during the intro window cheaper than 4 6 but the higher token consumption means you should measure your real cost rather than trust the headline do that math then switch for the work that fills most of a developer s day this is the model to reach for now and if you re on a free or pro plan it s already the one running by default top comments 0 subscribe personal trusted user create template templates let you quickly answer faqs or store snippets for re use submit preview dismiss code of conduct report abuse are you sure you want to hide this comment it will become hidden in your post but will still be visible via the comment s permalink hide child comments as well confirm for further actions you may consider blocking this person and or reporting abuse maksim danilchenko follow joined mar 30 2026 more from maksim danilchenko i benchmarked my ai carb counter on 400 meal photos 8 models 1 holdout set ai llm machinelearning testing kimi k3 review the 2 8t open model that beats claude on paper kimik3 moonshotai openweightmodels aicoding how to build an ai agent in go from scratch no framework go agents llm anthropicapi dev community a space to discuss and keep up software development and manage your software career home dev challenges dev videos dev education tracks dev help advertise on dev organization accounts dev showcase about contact free postgres database dev shop mlh code of conduct privacy policy terms of 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