161–170 of 364 posts
I clicked through and it showed Qwen at the top at 55.4 compared to 55.3 for Opus Max. I have a screenshot. Then I clicked away and back, and now it goes Qwen second, with 58.4, to Opus Max at top with 59.2. I have screenshots of both. The description above the chart is the same in boh cases: > Artificial Analysis Agentic Index > Represents the weighted average of agentic capabilities benchmarks in the Artificial Ana…
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It's not enough that it's better? Many providers will host it and will compete on price. It also can't easily be taken away because one company (or one government) decides they don't want it around any more. People can fine-tune it for particular workloads.
They cherrypicked benchmarks. The ONE weighed benchmark where is beats Opus5 by 0.1 points is what was linked because that's how propaganda works. The Agentic Index that includes the full benchmark suite has it in 5th place. Might as well use gpt-sol.
I stopped paying attention to self-published benchmarks when Apple started using those non-sensical performance graphs with "relative performance" as a vertical axis when announcing a new chip.
Opus is still first in Intelligence Index followed by Fable, GPT 5.6, Kimi K3 then Qwen 3.8 max. https://artificialanalysis.ai/#intelligence Our leaderboard combines Arena ELO, AA Intelligence index, latency and speed and goes: #1 Opus 5 #2 Kimi K3 #3 Qwen3.8 Max #4 GPT 5.6 Sol Source: http://pellmell.ai/leaderboard . This jumps around a lot based on the top throughput and latency of whatever provider happens to be b…
All these "intelligence" benchmarks miss something extremely important when using an LLM in a code-agent harness: How it communicates with you about what it did. Opus-5 is practically unusable (for complex tasks) in this sense - its updates are voluminous, and dense with cryptic language (there are numerous reddit threads complaining about this, so it's not just me). I often have to ask it to re-state concisely in pl…
Any benchmark showing Opus 5 as the best just loses credibility for me. Anyone who's actually used Opus 5 daily knows what I'm talking about.
I clicked through and it showed Qwen at the top at 55.4 compared to 55.3 for Opus Max. I have a screenshot. Then I clicked away and back, and now it goes Qwen second, with 58.4, to Opus Max at top with 59.2. I have screenshots of both. The description above the chart is the same in boh cases: > Artificial Analysis Agentic Index > Represents the weighted average of agentic capabilities benchmarks in the Artificial Ana…
They JUST updated their methodology: https://artificialanalysis.ai/methodology/intelligence-bench... Edit to provide AA's article explaining it: https://artificialanalysis.ai/articles/artificial-analysis-i...
What I'm really excited for is the 27B model. 3.6 is still the king of local, and if 3.8 makes the same improvements it could really legitimately make local viable as a default. I'd love to run a perpetual agent on 3.8 that's locally driven.
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All these "intelligence" benchmarks miss something extremely important when using an LLM in a code-agent harness: How it communicates with you about what it did. Opus-5 is practically unusable (for complex tasks) in this sense - its updates are voluminous, and dense with cryptic language (there are numerous reddit threads complaining about this, so it's not just me). I often have to ask it to re-state concisely in pl…
Is that what it feels like when the models get smarter than us?
Earlier quoted context omitted.
All these "intelligence" benchmarks miss something extremely important when using an LLM in a code-agent harness: How it communicates with you about what it did. Opus-5 is practically unusable (for complex tasks) in this sense - its updates are voluminous, and dense with cryptic language (there are numerous reddit threads complaining about this, so it's not just me). I often have to ask it to re-state concisely in pl…
Is that what it feels like when the models get smarter than us?
Clearly the weighting of those things depends on the usecase
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And at that cost they're still not profitable. It's going to be a bumpy road ahead...
I think profitability is a matter of accounting. Inference is where money is made, but training is where money is spent. We keep getting new models every few months, but frankly the old models are still quite usable. I suspect labs will soon start specializing in expert models per use case so they can increase the lifespan of individual models, and change the profitability per model.