Claude 4.5 Sonnet vs GPT-5: Which Model Should Developers Choose?

GPT-5 (high) is the stronger default for developers who need measured reasoning performance, lower blended cost, and a documented API surface. The Artificial Analysis snapshot reports a 34.7 intelligence index for GPT-5 (high) against 29.3 for Claude 4.5 Sonnet, while the math index is 94.3 for GPT-5 (high) against 37 for Claude 4.5 Sonnet. Data provided by https://artificialanalysis.ai/ supports these comparative measurements.

Claude 4.5 Sonnet remains a reasonable choice when a team already depends on Anthropic’s platform or prefers its established product positioning. Anthropic describes Sonnet as a balance of capability, speed, and cost for developers, although the supplied model overview does not provide a complete model-specific capability row. Anthropic’s model overview supports that limitation.

The practical decision is therefore asymmetric. GPT-5 (high) has stronger supplied benchmark evidence and lower listed token prices. Claude 4.5 Sonnet has less model-specific evidence in the supplied official material, so its case depends more on existing platform fit than on a demonstrated advantage in this comparison.

GPT-5 (high) offers the clearer engineering case because its official documentation exposes model identity, parameters, tools, endpoints, and published evaluations. OpenAI positions GPT-5 for coding, reasoning, and agentic tasks, and documents reasoning effort, verbosity, function calling, structured outputs, streaming, and custom tools. GPT-5 for developers and GPT-5 model documentation provide those details.

Claude 4.5 Sonnet is harder to evaluate from the supplied evidence because Anthropic’s overview discusses Claude capabilities broadly without mapping every capability to this specific non-reasoning model. The supplied material does not establish a model-specific context window, maximum output, tool limit, API parameter set, or official benchmark result for Claude 4.5 Sonnet. Anthropic’s model overview is the relevant source, and its omission is itself important for selection.

GPT-5 (high) also has a more explicit version-status risk. The current alias remains documented, but the fixed snapshot is marked Deprecated and the model page recommends a later generation. GPT-5 model documentation supports that conclusion. Claude 4.5 Sonnet is still present in Anthropic’s pricing table without a retired label, yet the supplied material does not prove that it has a stable API alias for the requested slug. Anthropic pricing and Anthropic’s model overview leave that point unresolved.

The missing evidence matters more than a polished feature checklist. The supplied sources do not directly answer which model produces fewer production regressions, handles a particular language stack better, or maintains higher tool-call reliability. Those questions require a task-specific evaluation.

GPT-5 (high) is the better-supported performance choice for coding and mathematical work, but the available comparison cannot establish a universal winner for every production workload. The Artificial Analysis snapshot gives GPT-5 (high) a coding index of 37.8, an intelligence index of 34.7, and a math index of 94.3. Claude 4.5 Sonnet has no coding index in the snapshot, plus an intelligence index of 29.3 and a math index of 37. Data provided by https://artificialanalysis.ai/ supplies the comparison data.

The math result is the clearest selection signal. A large gap in a reasoning-heavy evaluation can reduce the need for retries, external verification, or multi-step correction, especially in tasks where an incorrect intermediate result invalidates the final answer. It does not prove that GPT-5 (high) will be better for every coding repository, because the snapshot does not provide a directly comparable Claude coding score.

Latency does not create a tradeoff here. Both models report 0.3 seconds in the data snapshot, so a developer choosing between them should not expect the listed latency field alone to change the user experience. Output speed is unreported for both models. That omission prevents a reliable conclusion about streaming smoothness, long-response completion time, or throughput under sustained load.

Community evidence is also uneven. A Reddit user reported that GPT-5 helped with small debugging tasks but found it too terse for complete applications and UI generation, while comments described possible hallucinations or incorrect edits in complex existing repositories. The Reddit first-impressions thread documents those observations, but it is a single uncontrolled report. No equally reliable community evidence was supplied for Claude 4.5 Sonnet non-reasoning, so the evidence gap should not be mistaken for proof of a Claude advantage.

GPT-5 (high) is the cost-efficient default when the workload resembles the supplied blended-token mix, but Claude 4.5 Sonnet can still make sense if its output quality prevents expensive retries. The data snapshot lists GPT-5 (high) at $3.4375 per 1M blended tokens versus $6 for Claude 4.5 Sonnet. Data provided by https://artificialanalysis.ai/ is the source for that comparison.

The price advantage is not limited to blended usage. GPT-5 (high) is listed at $1.25 per 1M input tokens and $10 per 1M output tokens, while Claude 4.5 Sonnet is listed at $3 and $15. This favors GPT-5 (high) in applications that send large prompts, generate substantial answers, or do both. The advantage becomes less decisive if Claude’s responses need fewer correction calls, but the supplied material contains no production retry-rate data for either model.

Anthropic’s pricing structure adds deployment details that can reverse a narrow cost estimate. Regional and multi-region endpoints carry a 10% premium over the global endpoint, and cache writes and cache hits use different price multipliers. Anthropic pricing supports those conditions. A team with strict data-region requirements therefore needs to price the actual endpoint and cache behavior, not only the base token rates.

GPT-5 (high) has documented cached-input pricing at $0.125 per 1M tokens, but the supplied evidence does not establish that the two vendors’ caching features behave identically in a real application. Cost conclusions should therefore use the request mix, cache hit pattern, region, and retry behavior of the intended workload.

GPT-5 (high) is the recommended first evaluation for coding assistants, mathematical analysis, structured agents, and cost-sensitive production APIs. Its supplied intelligence index is 34.7, its math index is 94.3, and its coding index is 37.8. Its official documentation also describes function calling, structured outputs, streaming, adjustable reasoning effort, and custom tools. GPT-5 for developers and GPT-5 model documentation support these capability claims.

Claude 4.5 Sonnet is the more defensible candidate when the team already operates on Anthropic’s platform, has validated its output on an internal task set, or values a currently listed model without a supplied retired label. Anthropic lists Claude 4.5 Sonnet at $3 input and $15 output per 1M tokens, and its official material describes text and image input, text output, multilingual ability, and vision across the Claude family. Anthropic pricing and Anthropic’s model overview support those points. The supplied evidence does not map every family-level capability specifically to the non-reasoning model.

GPT-5 (high) should not be selected solely because a community post reports fast debugging. The same post reports weaker completeness for full applications and possible incorrect edits in complex repositories. The Reddit report is useful as a risk prompt, not as a general benchmark.

The final choice should be made with a narrow bake-off using representative repository tasks, tool calls, long-context prompts, and failure recovery. The supplied material does not provide controlled head-to-head results for those dimensions. If the bake-off is unavailable, GPT-5 (high) has the stronger evidence-backed default because it combines better supplied indices with lower listed prices. Teams should separately confirm migration plans because the fixed GPT-5 snapshot is marked Deprecated. GPT-5 model documentation documents that status.

GPT-5 (high) is easier to integrate from the supplied documentation, but teams still need to verify version policy, repository behavior, and real output throughput before production adoption. OpenAI documents the gpt-5 alias and several supported API surfaces, while the fixed snapshot is marked Deprecated. GPT-5 model documentation provides the relevant status information.

Claude 4.5 Sonnet requires more validation because the supplied official overview does not expose several model-specific limits or a stable alias for the requested slug. Anthropic’s model overview supports that evidence gap. Neither source set supplies a controlled comparison of reliability, output speed, long-context behavior, or error-recovery cost. Those unanswered questions are material selection risks, not minor documentation details.

Data provided by https://artificialanalysis.ai/ supplies the numerical comparison used in this article.