Opus 5.5 improves coding. Task cost is less clear

Claude Opus 5.5 launched on 22 September 2026 with lower token prices and a clear improvement in independent coding tests. That makes it worth trying, but the cost per finished task needs more care: Anthropic’s reported savings and Artificial Analysis’ maximum-effort measurements come from different workloads and settings. I would start at medium effort. [1] [2] [3]

How much better is Opus 5.5 at coding?

The independent coding result is a substantial improvement over Opus 5: Artificial Analysis reports a gain of 10.6 percentage points on Terminal-Bench 4.0. The test gives an agent practical tasks in a terminal, so it is more relevant to repository work than a short coding question. [2]

Opus 5.5 improves the independently measured terminal-task success rate Opus 5.5 scores 59.6 percent, compared with 49.0 percent for Opus 5.

Terminal-Bench 4.0

Opus 5.5 59.6%

Opus 5 49.0%

Show the data as a table

Benchmark	Opus 5.5	Opus 5
Terminal-Bench 4.0	59.6%	49.0%

Artificial Analysis runs all 66 tasks with mini-swe-agent and averages single-attempt success over three repetitions. Keeping that agent software consistent makes this a useful comparison. It still measures that particular setup, rather than every way someone might use Claude Code. [2]

The CursorBench results published by Anthropic point in the same direction: 57.8% at maximum effort, compared with 46.6% for Opus 5. Even the medium-effort Opus 5.5 result, 52.5%, exceeds that older maximum-effort score. Cursor ran this evaluation in its own agent software, according to Anthropic’s system card. That makes it useful additional evidence, but a separate setup from the Artificial Analysis test above. [10]

That medium result interests me more than another maximum-effort win. In my assessment of Opus 5 as an implementer, the problem was how much supervision strong code could still require. Better output with less reasoning would be useful; better output after a much longer run is a different trade.

Is Opus 5.5 actually cheaper per task?

Opus 5.5 is cheaper per input and output token, but a lower task bill depends on how much work it does. Claude Platform Docs lists $4 per million input tokens and $20 per million output tokens, down from $5 and $25 for Opus 5. Anthropic reports roughly 40% lower typical task cost because the new model also uses fewer tokens in its evaluations. [5] [1]

Artificial Analysis provides a useful counterweight. Its current Intelligence Index rises from 51 to 58, while the measured cost per index task rises slightly from $5.86 to $5.98 at maximum effort. Those are tasks in its evaluation suite, not a prediction of your average coding bill. [3] [4]

The result supports paying roughly the same for higher measured capability in this setup. It does not contradict Anthropic’s savings on other workloads. It does mean that I would not put a universal 40% saving into a budget. The index version matters too: these are both v4.3.2 scores, so an older Opus 5 score from a previous index cannot be compared directly with 58.

Anthropic’s prompting guide helps explain why effort deserves attention. At the same named setting, Opus 5.5 can think more per turn than Opus 5, especially at xhigh and max. The guide recommends testing effort levels again instead of carrying over the old choice. It also says medium can match or exceed Opus 5 high on coding and knowledge work. [6]

My practical reading is to buy the reasoning a task needs. A default of maximum effort would hide whether the new model can finish the same job with less work.

What does Reddit say about using Opus 5.5?

The most useful launch-day Reddit reports concern shorter answers and easier interaction, with disagreement about whether coding failures have improved. These are self-selected reports from 22 September, not a measured failure rate. I have not run a controlled hands-on comparison for this article.

In r/ClaudeCode, one user published responses to the same prompt across several Opus generations and found 5.5 easier to read than 5. That is a narrow example, but it gives the reader something concrete to inspect rather than a claim that the model simply feels smarter. [7]

A separate r/ClaudeAI thread praises speed and the ability to spot UI problems. Its comments include a report of the model getting stuck in an editing loop. Another release thread contains both faster-response reports and a complaint about incomplete implementations and ignored project instructions. [8] [9]

I take that as a reason to test communication and instruction-following separately from code quality. A model can produce a better patch and still make the session unpleasant. The distinction is also why my Opus 5 workflow assessment spends so much time on task scope and existing instructions.

What should developers check before switching?

An API migration needs a little more work than replacing the model name. Opus 5.5 uses claude-opus-5-5, defaults to medium effort, and always enables adaptive thinking. The overview also documents changes to tool handling, including errors for forced tool use. Existing agent integrations should check those migration notes before switching. [5]

For ordinary Claude Code use, I would start with an existing bug and a known acceptance test, then record whether medium effort produces a patch I can accept. I would also count corrections: having to repeat the same instruction is part of the cost even when it does not appear on an API invoice.

The alternative has changed on the same day. GPT-6 Sol and Luna’s price cuts and benchmarks make a different case for everyday work, with smaller intelligence-index gains and much lower token rates. I would compare them on a task I understand well enough to review, then choose the model that gets it accepted with the least intervention.