Claude 4.1 Opus (Reasoning) vs GPT-5 (low): The Ultimate Performance & Pricing Comparison

Claude 4.1 Opus (Reasoning) is the stronger premium signal, while GPT-5 (low) is the safer value choice for most new developer workloads. The benchmark snapshot gives Claude an Artificial Analysis Intelligence Index score of 33.7 and GPT-5 (low) a score of 31.2, while GPT-5 (low) leads the Math Index with 83 versus Claude's 80.3. The same snapshot reports 0.3 seconds of latency for each model, with no usable output-speed result for either model.\n\nThe harder issue is operational rather than purely technical. Anthropic's model overview does not identify the exact Claude slug used in the snapshot, and OpenAI's model catalog does not list gpt-5-low. Anthropic's pricing page does list Claude Opus 4.1, but marks it retired with Bedrock and Google Cloud exceptions. OpenAI's pricing page does not provide a current price for GPT-5 (low).\n\nThat creates a clear selection rule: treat the benchmark data as a useful comparative snapshot, but verify model identity, access, and billing in the deployment channel before committing production traffic. Data provided by https://artificialanalysis.ai/

GPT-5 (low) is the better default for cost-sensitive applications, while Claude 4.1 Opus (Reasoning) remains attractive when the higher Intelligence Index score matters more than price. The benchmark snapshot reports GPT-5 (low) at $3.4375 per 1M blended tokens, compared with $30 for Claude. Input pricing is $1.25 for GPT-5 (low) and $15 for Claude, while output pricing is $10 and $75 respectively.\n\nClaude's advantage is narrower but meaningful in the available evaluation data. Its Intelligence Index score is 33.7, compared with 31.2 for GPT-5 (low). That signal may matter for broad reasoning, planning, or multi-step work, but the supplied material does not explain the benchmark tasks, variance, or relationship to a particular coding workflow. GPT-5 (low) scores 83 on the Math Index, compared with 80.3 for Claude, so Claude should not be treated as a universal reasoning winner.\n\nThe operational evidence changes the recommendation. Anthropic officially presents Claude Opus 4.1 as retired, with Bedrock and Google Cloud called out as exceptions. The official Anthropic overview also does not confirm the exact model name, API ID, alias, context window, or output limit used by the comparison. OpenAI's official catalog and pricing documentation do not confirm gpt-5-low as a current callable model.\n\nThe comparison therefore has two layers. On the measured snapshot, GPT-5 (low) wins value and math, Claude wins the Intelligence Index, and latency is tied. On current platform evidence, neither label is sufficiently documented for an assumption-free production integration. Developers should validate access first, then run representative tasks, then compare total cost under their real input and output mix. Artificial Analysis is the stated provider of the comparison data.

Claude 4.1 Opus (Reasoning) has the stronger broad intelligence result, but GPT-5 (low) has the stronger math result and the available evidence cannot predict coding quality with confidence. Claude's Intelligence Index score of 33.7 exceeds GPT-5 (low)'s 31.2, while GPT-5 (low)'s Math Index score of 83 exceeds Claude's 80.3. Those results point to different strengths rather than a single performance hierarchy.\n\nFor developers, the practical meaning depends on task composition. A workload that asks a model to maintain a broad plan, reconcile conflicting requirements, or make several kinds of judgments may benefit from the higher Intelligence Index signal. A workload dominated by symbolic manipulation, numerical checking, or math-heavy transformations may favor GPT-5 (low)'s Math Index result. Neither interpretation proves superiority for code generation, debugging, repository navigation, tool use, or production incident response.\n\nThe chart below should be read as directional evidence, not as a substitute for task tests. The supplied research found no official model-specific benchmark documentation for either exact label. Anthropic's overview does not publish Claude 4.1 Opus-specific benchmark scores, and OpenAI's model documentation does not list GPT-5 (low) or provide model-specific benchmark detail for it.\n\nLatency does not separate the options in this snapshot. Both models are reported at 0.3 seconds, and neither has a reported median output speed. That means interactive responsiveness remains unresolved. A model can feel faster because it starts producing earlier, streams more consistently, or completes fewer retries, but the supplied data does not measure those behaviors.\n\nA developer evaluation should therefore test complete task outcomes. Measure accepted patches, test-pass rate, correction turns, tool-call failures, and time to a usable result. Those measurements would answer questions the current brief leaves open, including whether Claude's broad score produces better repository-level decisions or whether GPT-5 (low)'s math advantage reduces verification work. Community evidence is also insufficient: the research found no reliable, methodologically clear public discussion for either exact model label.

GPT-5 (low) is the clear price leader in the supplied snapshot, but its cost advantage only matters if its access and task success are real in the intended environment. The reported blended price is $3.4375 per 1M tokens for GPT-5 (low), compared with $30 for Claude 4.1 Opus (Reasoning). The input prices are $1.25 and $15, and the output prices are $10 and $75.\n\nThe chart below already shows those price points, so the important question is what they buy. A low token price can lose its advantage when the model needs more retries, produces longer unusable answers, makes more tool-call mistakes, or requires a stronger verification loop. The supplied data does not measure any of those costs. It also does not establish a current official price for GPT-5 (low), because OpenAI's pricing documentation does not list that label.\n\nClaude's published pricing is easier to interpret but its availability is narrower. Anthropic's pricing documentation lists Claude Opus 4.1 at $15 per 1M input tokens and $75 per 1M output tokens. It also lists prompt-caching prices, including $1.50 per 1M tokens for cache hits and refreshes. Those caching terms could change the economics of applications that repeatedly send a stable system prompt, codebase context, or policy bundle. The research does not establish whether the exact comparison slug is still provisioned under those terms.\n\nThe cost conclusion can flip under three conditions. First, GPT-5 (low) may not be callable under the name being evaluated. Second, a workload with high output volume magnifies the output-price difference and may need fewer Claude retries to reach an accepted result. Third, Claude caching may reduce repeated-context costs in a workload designed around cache reuse. None of these conditions can be quantified from the supplied brief.\n\nUse the benchmark prices for initial screening, not procurement approval. Confirm the actual model ID, provider surcharge, caching behavior, rate limits, and billing unit in the chosen channel. Then compare cost per accepted task, not cost per token alone.

GPT-5 (low) is the recommended starting point for most new applications, provided the deployment channel confirms the model identity and price. Its reported blended price of $3.4375 per 1M tokens is substantially below Claude's $30, and its Math Index score of 83 leads Claude's 80.3. That combination makes it a rational first candidate for high-volume classification, structured transformation, code assistance with strong tests, and math-oriented automation.\n\nClaude 4.1 Opus (Reasoning) is the better candidate for teams that can access it through an approved Bedrock or Google Cloud path and have a workload where broad reasoning quality is worth premium spend. Its Intelligence Index score of 33.7 leads GPT-5 (low)'s 31.2. Claude's official pricing entry also confirms a specific enterprise availability exception, although the exact model identity and current API details still require verification.\n\nDo not choose Claude solely because its Intelligence Index is higher. The math result favors GPT-5 (low), the latency result is tied at 0.3 seconds, and the research provides no reliable community evidence about coding, long-context work, tool use, or failure behavior. Do not choose GPT-5 (low) solely because the snapshot price is lower. OpenAI's current model directory does not list the exact label, and the official pricing page does not confirm a current price.\n\nA staged evaluation is the safest path. Start with a small set of real developer tasks, including bug diagnosis, patch generation, test repair, API integration, and documentation changes. Score the final accepted outcome, reviewer effort, retry count, latency experience, and provider access. Keep the model that wins the complete workflow, even if it loses one isolated benchmark.\n\nThe evidence supports a provisional ranking rather than a definitive production verdict. GPT-5 (low) leads on apparent value and math. Claude 4.1 Opus leads on the available general intelligence signal. Platform verification is the deciding gate because both official documentation sets leave important identity or availability questions unanswered.

Claude 4.1 Opus (Reasoning) has a clearer documented retirement status, while GPT-5 (low) has a clearer benchmark value signal but weaker official identity evidence. Anthropic's official pricing page labels Claude Opus 4.1 as retired, with Bedrock and Google Cloud exceptions. The official model overview does not confirm the comparison slug claude-4-1-opus-thinking. OpenAI's model catalog does not list gpt-5-low, and its pricing page does not list a current GPT-5 (low) price.\n\nThat asymmetry matters for engineering risk. A retired model can create migration pressure, reduced access, or provider-specific behavior even when an approved exception remains available. An unlisted model label can create a different risk: the benchmark may represent a configuration, alias, or historical entry that cannot be reproduced through the public API. The supplied research cannot determine which explanation applies to GPT-5 (low).\n\nThe community signal is equally limited. No reliable Reddit, Hacker News, or X discussion with a clear test method was found for either exact model label. No verified source establishes distinctive coding habits, speed perception, long-context behavior, tool-call reliability, or failure modes. Developers should record those findings from their own acceptance tests rather than convert the absence of discussion into a positive or negative claim.\n\nThe most important unanswered question is reproducibility. Before adopting either model, confirm the exact provider, model ID, reasoning configuration, context limit, output limit, tool support, rate limits, and price. The supplied materials confirm none of those details for GPT-5 (low), and they confirm only part of them for Claude 4.1 Opus. That evidence gap is itself a selection factor.