6.1 Sol Is the Best Value? The Comments Section Has Already Argued Itself Into a Model Task-Allocation Chart

Haider posted an Artificial Analysis model comparison table on X with a very short caption: 6.1 Sol is still the model with the best balance of cost and performance, change my mind. He added that Opus 5.5 is the best model Anthropic has released so far, but he can still catch it in logic traps. It is more expensive, so he only uses it occasionally for specific review tasks, not as his main model. Original post link: https://x.com/haider1/status/2114089545729736832

First, let us lay out the hardest data in the table. In the Max tier, Opus 5.5's Intelligence Index is 58, Sol's is 52, and Astra's is 53; cost per task is $5.98 for Opus, $0.72 for Sol, and $3.26 for Astra; output speed is 96 tokens/s for Opus and 57 tokens/s for Sol. On Terminal-Bench 4.0, both Opus and Astra are at 60% in the Max tier, while Sol is at 56%. On GDPval-AA v2.1 Elo, Opus Max tier is 1866, Sol is 1596, and Astra is 1574. The table comes from Artificial Analysis. Put the numbers together, and the conclusion is not so tidy: Opus has a higher ceiling, Sol is cheap, and Astra sits in between.

The comment section did not follow Haider's change my mind. Some people directly picked sides over the division between everyday and important tasks. James said he uses Sol 6.1 for everyday tasks and brings in Opus 5.5 when things really matter. Franciele said the cost gap makes it impossible for her to use Opus as her everyday main model, even though it handles strange edge cases better. Avyn put it more bluntly: cost-performance is the only leaderboard that matters for everyday use, and Sol keeps winning; Opus is the better model, Sol is the smarter purchase. JK offered a crude analogy: 5.5 is a senior developer, while Astra and Sol are juniors.

There were also plenty of dissenters. Zach said 6.1 Sol is garbage and will ruin what Astra produces. Ross said his findings are completely the opposite. Greg said Opus 5.5 makes all OpenAI models look expensive and hard to use. Trident focused on the harness, meaning the runtime framework around the model: Sol's harness is better, and after the time limit Claude will spend a huge number of tokens re-caching—say, 400k tokens—losing 7% before it even starts working. Ga b added something often overlooked: Opus frequently refuses to do what you want it to do. M broke down 'best balance': it is very subjective; compared with DeepSeek it may be too expensive, while compared with Anthropic's own models it feels penny-wise and pound-foolish. It is a solid mid-range product.

Disagreements over specific tasks are more useful. Steve ran a 3D sectioning test, and the differences came down to boring details: overlapping labels and a boxier shell. He said a cheap model can produce a draft, but not the final render. So his division of labor is reversed: the cheap model writes scripts, and Opus handles anything with geometry. Ben mentioned that factory scenarios only look at validated output; this is where you can tell apart models that can interact and talk from models that communicate less but get things done. Chinese netizen tian hongyu has a more detailed workflow: Opus does video and copywriting, often giving unexpected creative ideas; when doing projects, he has it write proposals and plans, GPT reviews them, the confirmed plan is handed to GPT for execution, and then he opens another terminal and has another GPT review it.

The logic trap Haider mentioned was not elaborated on. Chahat asked whether it was puzzle-style prompts or actual errors in review work. Pragnyan said that set of logic traps is more like an evaluation than half the leaderboards and suggested making it public. This thread is more interesting than taking sides: leaderboards test uniform problems, while the pitfalls encountered in real workflows are often personal.

The most valuable part of this discussion is not ranking 6.1 Sol and Opus 5.5. Some people treat models as everyday employees, some as reviewers, and some as outsourcing for geometry and copywriting. Costs need to be calculated per task, and the cost of failure also needs to be calculated per task. Which model to choose as your main one depends first on which mistake you fear more: spending too much on everyday tasks, or saving money in the wrong place on critical tasks.