Claude Sonnet 5.5: price, context, and demonstration results

In the material presented, Claude Sonnet 5.5 is described as an Anthropic language model. Below, the pricing and context cited from an Anthropic page are discussed separately, along with the demonstration author's observations. These details have different levels of verification: the primary quotation from the source material is absent from the data provided.

Claude Sonnet 5.5: price and context

According to the demonstration host, Claude Sonnet 5.5 costs $2 per million input tokens and $10 per million output tokens. The review materials also state that, on Anthropic's pricing page, Claude 4.6 and later models are associated with a context window of 1 million tokens, and that Claude Sonnet 5.5 appears in the list of models. There is no direct quotation from that page in the provided materials, so these details cannot be considered a separately verified excerpt from the primary source.

This information is sufficient for a preliminary calculation of token costs, but not for determining the total cost of a specific task. Usage depends on the volume of the input data and the model's response, and the cost of using tools in a separate scenario cannot be determined from the materials provided.

The pricing and context details are cited from Anthropic's pricing page. The page itself and the primary quotation were not separately verified in the provided materials.

What the demonstration showed

The author compared individual Sonnet results with Opus and described impressions from the tasks. The provided materials contain no confirmed Terminal Bench results or comparison of the models on this test.

The test version, model settings, number of runs, evaluation criteria, and variation in the results are not provided. Without this information, it is impossible to determine whether the individual impressions reflect a consistent advantage or the specifics of a particular run conducted under demonstration conditions, where the task set and evaluation method were limited.

The author's observations apply only to the tasks shown. They do not prove that the model will be the best choice for any development workflow.

Practical observations and limit usage

The author said that Sonnet's output quality reminded him of Opus and that limits were used relatively slowly. This is a personal observation, not a measurement that can be extrapolated to another account, operating mode, or set of tasks.

The demonstration included an animation of ice cream with chocolate dripping over it, an aquarium with fish and other animals, a Mario-style game, and a three-dimensional fan with speed control. The author rated the ice cream animation as high-quality and noticed no errors in the fan's operation. The expected mushroom did not appear in the game; the author also noted minor shortcomings in the level.

According to the author's description, the aquarium contained fish, rocks, bubbles, and swaying grass; after a request for improvement, additional fish species, a crayfish, a crab, and snails appeared. The author watched the crab climb onto a rock and the crayfish climb down from it. The author only hypothesized that this behavior was caused by limitations in the logic or by collisions, so this explanation should be considered unconfirmed.

The author reported that after checking, he saw usage 11,5 and considered the limit usage low. Later, he gave the value 7% and another indicator, “one,” whose meaning he did not explain. The units of measurement and the exact values of these indicators have not been established in the provided materials, so the figures cannot be compared with other people's limits or used as an exact cost estimate.

Did Anthropic fix limit usage? The author supposed that the company had made changes, but the provided materials contain no confirmation of this cause. His view that Anthropic's models offer a favorable balance between quality and limits is also based on personal observations rather than a systematic comparison.

Comparison with Opus and GPT-6 Soul

In the author's view, Sonnet 5.5 was slightly inferior to Opus, although in some tasks the results seemed similar to him. This is a subjective conclusion based on a limited set of tests, not the result of a comparison using a consistent methodology.

The author also stated that Sonnet was better than GPT-6 Soul. The provided materials contain no comparable results for identical prompts using a consistent methodology, so this statement should be considered the author's personal assessment rather than a confirmed comparison result.

Astra was not compared in this material. The author said that he did not know why this comparison was absent. Any explanations for the reason remain unconfirmed.

A single demonstration is insufficient for making a practical choice: the available materials contain no comparison of the models on identical tasks using a consistent methodology. The author evaluated individual results and limit usage, but these observations do not show how the models would perform on a different task or for another user.

What cannot be established from the available data

The author's impressions cannot establish that Sonnet 5.5 is better than Opus for all types of programming. Nor can they confirm that the model always uses fewer limits or outperforms GPT-6 Soul.

The provided materials contain no repeated runs using identical model versions and prompts, no description of the settings, and no transparent method for calculating quality. This is insufficient to establish a model's advantage on a particular team's tasks, especially if its workflows differ from those shown in the demonstration.

Context and multimodal input

The review materials state that, on Anthropic's pricing page, Claude 4.6 and later models include a context window of 1 million tokens, and that Claude Sonnet 5.5 appears on the same page. Since the primary quotation from the page is absent from the provided materials, this should be treated as information cited by the review's author, not as a separately confirmed excerpt from the source. The available materials do not confirm support for image or document input.

The stated pricing and context-window size make it possible to estimate the approximate order of costs, but calculating a specific scenario requires data on the volume of input and output tokens. The cost of tools and any other possible expenses cannot be determined from these materials.

Brief conclusion: the review materials, citing Anthropic's pricing page, state the price of Claude Sonnet 5.5 and a context window of 1 million tokens, but the primary quotation from that page is absent. The comparisons with Opus and GPT-6 Soul, as well as the conclusions about saving limits, remain the author's impressions rather than the results of systematic testing.