Claude Opus 5 vs GPT-4: Which Model Should Developers Choose?

Claude Opus 5 is the safer default for new developer workloads because its capability, pricing, API behavior, and lifecycle are currently documented, while GPT-4 has major evidence gaps.

The data snapshot gives Claude Opus 5 an Artificial Analysis intelligence index of 60.7 and a coding index of 78. GPT-4 scores 7 and 13.1 on the same two indexes. Those results do not prove that every application will perform better with Claude Opus 5, but they create a large starting advantage for coding-heavy selection decisions.

The operational difference is equally important. Anthropic documents Claude Opus 5 as an active model with a stable API ID and alias, and says it is positioned for complex agentic coding and enterprise work. See the Claude Opus 5 announcement, the models overview, and the model deprecations page.

OpenAI’s reviewed model directory does not provide current GPT-4 capability parameters, context information, output limits, or API behavior. The OpenAI models documentation also focuses its current product listing on newer model families. That does not establish that GPT-4 cannot work. It does establish that a new buyer has less verified information before committing to it.

Claude Opus 5 offers the stronger documented case for new development, while GPT-4 remains difficult to evaluate because current official evidence is incomplete.

Decision area	Claude Opus 5	GPT-4
Coding index	78	13.1
Intelligence index	60.7	7
Blended price	$10 per 1M blended tokens	$37.5 per 1M blended tokens
Input price	$5 per 1M input tokens	$30 per 1M input tokens
Output price	$25 per 1M output tokens	$60 per 1M output tokens
Median output speed	60.088 tokens per second	Not reported
Measured latency	0.3 seconds	0.3 seconds
Current official status	Active, with documented model identity	Not clearly established in the reviewed sources

Claude Opus 5 is documented as accepting text and image inputs, producing text, supporting multiple languages, and offering vision capabilities through several deployment channels. Anthropic also documents adaptive thinking and configurable effort levels in the models overview and Opus 5 changes documentation.

GPT-4 has no comparable current parameter sheet in the reviewed OpenAI material. The OpenAI pricing documentation does not list GPT-4, GPT-4-0314, or GPT-4-0613. Therefore, the comparison favors Claude Opus 5 for verified procurement and integration readiness, not only for benchmark performance.

The main uncertainty is whether GPT-4 remains available to a particular account or endpoint under a specific compatibility arrangement. The reviewed sources do not answer that question directly, so teams should verify access before treating GPT-4 as a production option.

Claude Opus 5 is the stronger measured choice for coding and general intelligence, but its reasoning behavior can increase output management work.

The coding-index gap is large enough to change the architecture of a developer tool. A model with a coding index of 78 may require fewer retries, less manual correction, and less orchestration around repository changes than a model scoring 13.1 in the same snapshot. Those are practical implications, not guarantees. The data does not identify the benchmark tasks, error distribution, or your application’s workload, so teams should still test representative repositories and tool loops.

Claude Opus 5 also reports a median output speed of 60.088 tokens per second, while GPT-4 has no reported value in the snapshot. Measured latency is 0.3 seconds for both models. This suggests that the first response may begin under similar measured conditions, while Claude Opus 5 has the clearer evidence for sustained generation speed. The chart cannot show whether thinking overhead, tool calls, or long responses dominate total task time.

Anthropic says Claude Opus 5 defaults to adaptive thinking and that the token limit covers thinking tokens plus visible response tokens. Developers carrying forward a small output limit can therefore receive less visible content than expected. The same Opus 5 changes documentation says disabling thinking can cause tool calls to appear as ordinary text or expose internal XML tags, so turning reasoning off is not a free performance optimization.

Community feedback is divided. One ClaudeCode Reddit discussion describes strong performance on complex work but also reports verbosity, slowness, and overthinking. Another ClaudeAI Reddit discussion reports similar complaints, while acknowledging the lack of a standardized test method. A Hacker News discussion raises a related concern about pursuing an elaborate visual workflow without first confirming the model’s direct access. These reports are useful risk signals, not controlled evidence.

The reviewed sources provide no equivalent current community test for GPT-4. That absence should be treated as missing evidence, not proof that GPT-4 behaves better.

Claude Opus 5 is cheaper on every listed token price, but its reasoning and response style can still affect the total cost of a workflow.

The blended price is $10 for Claude Opus 5 versus $37.5 for GPT-4. Input pricing is $5 versus $30, and output pricing is $25 versus $60. These differences make Claude Opus 5 the clear economic starting point in the supplied snapshot, especially for workloads with substantial prompts, generated code, or repeated agent context.

The practical caveat is token volume. Anthropic documents that Claude Opus 5 has thinking enabled by default and that thinking tokens share the hard limit with the final response. A task that produces longer reasoning, more progress narration, repeated validation, or extra delegation can consume more tokens than a terse implementation. Anthropic documents these behavior changes in the Opus 5 changes documentation, while the community reports provide unstandardized examples of verbosity and overthinking.

Caching can change the cost profile for repeated prompts. Anthropic lists prompt-cache pricing in its pricing documentation, and states that prompts shorter than 512 tokens do not create a cache entry in the Opus 5 documentation. That means a developer tool with a large, stable repository prefix may have a different effective cost pattern from a short interactive request. The supplied data does not provide cache hit rates, prompt mix, retry counts, or output volume, so no more precise total-cost conclusion is justified.

GPT-4’s current price is another evidence problem. The data snapshot supplies comparison prices, but the reviewed OpenAI pricing page does not currently list GPT-4. Developers should not assume that a historical price, a compatibility endpoint, or a third-party route remains valid without checking their actual account and deployment path.

The cheaper model becomes more expensive only if its extra reasoning, retries, or oversized responses materially increase token consumption. The supplied evidence does not quantify that crossover point.

Claude Opus 5 is the recommended default for new coding and agentic systems, while GPT-4 should be selected only when an existing dependency has already been verified.

Choose Claude Opus 5 for repository-level coding agents, multi-step implementation tasks, enterprise automation, and workflows that benefit from adaptive reasoning. Anthropic explicitly positions it for complex agentic coding and enterprise work in the launch announcement. Its documented tool behavior, configurable effort levels, multimodal input support, and deployment availability give engineering teams concrete controls to evaluate.

Choose GPT-4 only when compatibility is the primary requirement and your team has confirmed the exact endpoint, model identity, access status, pricing, and behavior. The reviewed OpenAI sources do not establish those facts for current GPT-4 usage. That uncertainty matters more for a new build than for a legacy system that already has monitoring, regression tests, and a known provider route.

Claude Opus 5 still needs guardrails. Keep thinking enabled unless testing proves that a different configuration is safe. Set visible-output limits with its shared thinking-and-response budget in mind. Add concise-response instructions if response length affects cost or user experience. For security research, account for Anthropic’s stated restrictions on binary vulnerability scanning, penetration testing, and exploit generation, which are described in the Claude Opus 5 announcement.

A fair migration test should measure completed task success, patch correctness, tool-call validity, retry frequency, visible output length, and end-to-end time. The current materials do not provide those application-level measurements for either model. The comparison therefore supports a default choice, not a substitute for a workload-specific evaluation.

For most new developer products, the decision is straightforward: start with Claude Opus 5, then challenge that choice with a representative regression set. Keep GPT-4 only if verified compatibility or a measured application result outweighs the evidence gap.

Claude Opus 5 is the better starting point for most new developer evaluations because the available evidence covers capability, pricing, API behavior, and lifecycle status.

The remaining questions concern uncertainty rather than a missing headline winner. GPT-4 may still be present in a legacy or account-specific integration, but the reviewed official pages do not confirm its current availability, stable alias, context behavior, or current price. Claude Opus 5 has clearer documentation, yet its adaptive reasoning can increase token use and response-management work. Developers should validate those tradeoffs against their own tool loop and output requirements.