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100% Positive

Analyzed from 551 words in the discussion.

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#claude#model#opus#models#anthropic#more#index#fable#max#less

Discussion (21 Comments)Read Original on HackerNews

andy99•14 minutes ago
#1 in a very close race is way less useful when you have to walk on eggshells to avoid triggering censorship (“safeguards”) that either refuse or knock it down to another model. I’ve almost completely stopped using Claude (except some legacy workflows) for this reason, reliability matters more than scoring 61 instead of 57. To me Claude is the most compromised and unreliable model (between the censorship and the id checking - which I have not experienced personally), it’s not worth whatever slight benchmaxxing they did for the latest release.
afavour•2 minutes ago
What are you asking that you’re so regularly running into censorship?
firasd•25 minutes ago
Very interesting that one of the components is "AA-Omniscience Index"

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer.

This seems to be a good proxy for param size/density and the ranking breaks down as such: Claude Fable 5 (with fallback), Gemini 3.1 Pro Preview, Claude Opus 5 (Max), Grok 4.6 (high), Gemini 3.6 Flash, GPT 5.6 Sol (Max)

I've thought for a while that Gemini 3.x has 'big model smell'

hoppp•7 minutes ago
I didn't like it as much as fable. The coding style was a bit different and it way overbuilt the thing I asked from it.
chmod775•14 minutes ago
The more interesting finding is that it's still the second most expensive model (after Fable 5) by a long shot.

At least two models (GPT-5.6, Kimi K3) match its score (~1-2% diff) for half the cost.

aarondong•about 4 hours ago
Before getting too excited, take a look at the intelligence vs cost matrix: https://artificialanalysis.ai/models?intelligence-index-toke...
eli•32 minutes ago
Max is lot of extra reasoning. I wonder how many fewer tasks it solves on high. I bet that costs quite a lot less.
midnightbobarun•about 3 hours ago
5.6 Sol (max) being cheaper than all of these is wild, considering how good the output is too
nijave•about 1 hour ago
I think on swebench verified luna was only like 3% points lower for 1/5 the cost

Like 96% vs 93% or something

impulser_•32 minutes ago
It shouldn't be surprising OpenAI does have the most compute out of all the major labs. The only reason why Anthropic models are expensive is they are the most in demand models in the world and Anthropic is fighting for compute. The only way to you limit demand for your model is increasing API pricing this is also why Anthropic probably has great margin and probably is profitable compared to OpenAI.
scrlk•24 minutes ago
Not just compute for OAI, GPT-5.6 is more token efficient across the board vs the Anthropic equivalents: https://artificialanalysis.ai/models?intelligence-index-toke...

No wonder why Tibo can afford to hit the reset button liberally.

charcircuit•15 minutes ago
I also suspect there is a price fixing agreement between all of the inference providers for Claude (such as Amazon, Anthropic, Microsoft, etc).
giancarlostoro•about 1 hour ago
Probably because they made ASICs to run inference for less.
brookst•about 1 hour ago
Are those actually deployed at scale yet?
Schiendelman•about 2 hours ago
This must be on API costs, not counting the $100/200 tiers, right?
sggyamg•17 minutes ago
It's new, normal.
claude-ai•about 3 hours ago
On my end, Opus 5 is Haiku level vs. Opus 4.8 (good) and Fable (superb).

Gets confused by permission prompts, cannot debug a failing test it caused (Opus 4.8 got it right after, without tens of rounds "thinking").

reilly3000•31 minutes ago
Are you using Claude Code/CoWork or an API client? I’m curious if it has different training that makes it more effective with specific instructions/ tool calling methods that are only implemented in official harnesses.
pixelesque•21 minutes ago
I'm curious about this too, and it's difficult to get any information about this given everyone has different setups, workflows and use-cases.

I bizarrely had Opus 4.8 this week (in pi.dev within a podman container, using openrouter) start installing various python packages (and uv!) within the environment (not as root) when I asked it to code review some fairly basic Rust .rs files that were generally stand-alone (it did very nicely work out and write some stubs for them to build them and work out how they worked).

It only gave up with the weird Python installing stuff when it discovered one of the Python packages needed Tensorflow.

It seems pretty focused and persistent in continuing its initial approach, and I'm wondering if I need to alter some instructions / initial prompts to rein it in a bit...