Claude 4.5 Sonnet (Reasoning)

Claude 4.5 Sonnet (Reasoning) is most attractive when mathematical reliability matters more than raw throughput or the lowest token bill. Artificial Analysis places the model at 37 of 265 on its Math Index, which is a stronger showing than its general intelligence and coding positions. The model ranks 89 of 578 on the Intelligence Index and 66 of 202 on the Coding Index. Those results suggest a capable general model with a particular advantage in structured quantitative work, rather than an obvious default for every software task.

Anthropic describes the Claude family as supporting text and image inputs, text outputs, multilingual use, and visual capabilities through Claude API, Amazon Bedrock, Claude Platform on AWS, Google Cloud, and Microsoft Foundry (Model overview). That statement applies to the current Claude model family, not necessarily to every boundary of this specific model. Anthropic’s overview does not clearly provide Claude 4.5 Sonnet’s context window, maximum output length, vision limits, official benchmark results, or complete stable alias.

The practical conclusion is narrow but useful: this model deserves consideration for analysis, code review, mathematical reasoning, and tasks where a stronger answer is worth paying for. It deserves less automatic preference for high-volume generation, latency-sensitive user interfaces, or workloads that can use a much cheaper model. Data provided by https://artificialanalysis.ai/.

Claude 4.5 Sonnet (Reasoning) offers a credible middle ground for teams that need more reasoning strength than a lightweight model but cannot justify the highest-priced option. Its Math Index position, 37 of 265, is the clearest evidence for that role. Its Intelligence Index position, 89 of 578, indicates useful broad capability, but it does not establish category leadership. Its Coding Index position, 66 of 202, supports software use while leaving room for stronger coding-focused alternatives.

The adjacent models in the data brief clarify the tradeoff. Gemini 3.5 Flash-Lite has a similar Intelligence Index score of 36.5 and a lower blended price of $0.8500000000000001 per 1M tokens. Grok 4.20 0309 (Reasoning) also has an Intelligence Index score of 36.5, with a blended price of $3. GPT-5 Codex (high) has a slightly lower Intelligence Index score of 36.1, but a Math Index score of 98.7 and a blended price of $3.4375. These references do not prove that any one model wins every task. They show that Claude 4.5 Sonnet’s value depends on whether its particular reasoning behavior fits the workload.

Decision factor	Claude 4.5 Sonnet (Reasoning)	What the adjacent models suggest
General capability	Strong, but not dominant by rank	Similar intelligence scores are available elsewhere
Mathematical work	Its clearest measured strength	GPT-5 Codex (high) scores higher on the listed Math Index
Cost	Premium blended price	Several adjacent models cost less
Selection logic	Choose for task fit	Do not choose from brand familiarity alone

Claude 4.5 Sonnet (Reasoning) is best treated as a reasoning-oriented model with demonstrated mathematical strength and incomplete operational evidence. The model ranks 37 of 265 on the Artificial Analysis Math Index, compared with 66 of 202 on the Coding Index and 89 of 578 on the Intelligence Index. The spread matters because a single general score would hide the model’s more specific profile. Developers should expect the strongest case to involve problems that reward multi-step analysis, formal structure, or careful quantitative answers.

The data brief records 0.3 seconds to first token, but it does not report median output tokens per second. That makes the model’s interactive experience only partly measurable. A quick first token can still lead to a slow completion, especially in tasks that produce long reasoning or extensive code. Teams building chat interfaces, IDE features, or agent loops should therefore measure end-to-end completion time in their own workload before committing to a production default.

The coding rank supports use in code generation and review, but it does not justify assuming superior repository-level performance. The research brief contains no reliable community reports, reproducible community tests, or verified posts describing this model’s coding behavior, speed, or preferences. Anthropic’s official overview also does not list model-specific failure modes or known limitations (Model overview). Evidence is therefore strongest for comparative benchmark positioning, not for claims about debugging accuracy, tool use, long-context behavior, or production reliability.

A sensible evaluation should test representative bug fixes, mathematical transformations, structured extraction, and refusal-sensitive tasks. Those tests should record complete task success, revision rate, output length, and total elapsed time. The available brief does not provide those measures.

Claude 4.5 Sonnet (Reasoning) is difficult to justify for high-volume workloads unless its higher-quality answers reduce enough retries, human review, or downstream processing. Anthropic lists input pricing at $3 per MTok and output pricing at $15 per MTok (Pricing). The data brief gives a blended price of $6 per 1M tokens under its stated 3 to 1 input-to-output mix. That structure makes output-heavy applications especially sensitive to model choice.

The price is not automatically unreasonable. A developer tool that uses the model for difficult code changes, architecture analysis, or mathematical validation may benefit from fewer correction cycles. The evidence does not show whether those savings occur in practice. No reliable community testing or task-level cost analysis was found in the research brief. Teams should treat quality-adjusted cost as an open measurement question, not as an assumed benefit.

Prompt caching can improve repeated-context economics, but it is not free reuse. Anthropic lists 5-minute cache writes at $3.75 per MTok, 1-hour writes at $6 per MTok, and cache hits and refreshes at $0.30 per MTok (Pricing). Caching is most relevant when the same large instructions, repository context, or policy text recurs across requests. It is less helpful when prompts change substantially or requests are too infrequent to reuse context.

Regional and multi-region endpoints add a 10% premium for Claude Sonnet 4.5 and later models, while the Claude API defaults to the global endpoint (Pricing). That makes deployment configuration part of the cost decision. Compare endpoint requirements, cache behavior, output volume, and retry rates before declaring this model cost-effective.

Claude 4.5 Sonnet (Reasoning) is worth selecting when a workflow rewards mathematical reasoning, careful analysis, and broad multimodal model-family support. Its strongest listed result is the Math Index rank of 37 of 265. Its general Intelligence Index rank of 89 of 578 and Coding Index rank of 66 of 202 support a broader role, but they do not make it the obvious leader for general or coding work.

The model is a reasonable candidate for:

• Mathematical explanation, verification, and transformation tasks.
• Code review where correctness matters more than minimum cost.
• Technical analysis that benefits from a premium reasoning model.
• Workflows that can absorb a $6 blended price per 1M tokens.

The model is a weaker default for:

• Large-scale classification or summarization.
• High-volume user-facing generation.
• Applications where output throughput must be known before launch.
• Teams seeking the lowest-cost model with similar general benchmark positioning.

The recommendation should change if local tests show that cheaper adjacent models meet the same acceptance threshold. Gemini 3.5 Flash-Lite and Grok 4.20 0309 have Intelligence Index scores of 36.5, while GPT-5 Codex (high) reaches a Math Index score of 98.7 at a lower blended price than Claude 4.5 Sonnet. Those comparisons are directional because the brief does not provide identical evaluation coverage for every model.

Anthropic confirms that Claude 4.5 Sonnet remains listed in the current API pricing table and is not marked retired (Pricing). However, the research brief does not establish a complete stable alias, every cloud-specific model ID, or continued direct availability on each listed platform. Verify deployment details before implementation.

Claude 4.5 Sonnet (Reasoning) should enter production only after workload-specific validation resolves the evidence gaps around throughput, context behavior, and deployment identifiers. Anthropic’s model overview describes Claude-family capabilities but does not provide a complete model-specific specification for this version (Model overview). The available benchmark data is useful for positioning, but it cannot replace tests on the developer’s own tasks.

A short evaluation should compare answer acceptance, correction frequency, full completion time, output volume, and endpoint cost. It should include both easy and failure-prone examples. The research brief does not provide verified community test methods or reliable community reports for Claude 4.5 Sonnet (Reasoning), so those measurements must come from internal testing or a separately verified evaluation source.