Claude 4.1 Opus (Reasoning) vs GPT-5 mini (medium): Which Model Should Developers Choose?

Claude 4.1 Opus (Reasoning) leads the measured intelligence score, while GPT-5 mini (medium) offers the stronger math score and dramatically lower benchmark pricing. The comparison is therefore not a simple quality-versus-cost decision. It is also an availability and evidence decision.

The supplied Artificial Analysis snapshot reports Claude 4.1 Opus (Reasoning) at 33.7 on the Artificial Analysis Intelligence Index and GPT-5 mini (medium) at 30.9. GPT-5 mini (medium) leads the Artificial Analysis Math Index with 85 versus Claude 4.1 Opus (Reasoning) at 80.3. Both models show 0.3 seconds of latency in the snapshot, while output-speed values are unavailable.

The vendor documentation introduces a more serious qualification. Anthropic lists Claude Opus 4.1 as retired, with Bedrock and Google Cloud exceptions, at Claude pricing. OpenAI’s supplied model directory does not list gpt-5-mini or gpt-5-mini-medium, as shown by OpenAI Models. Developers should treat the measured comparison as useful evidence, but not as proof that either exact configuration is a safe new production dependency.

Claude 4.1 Opus (Reasoning) is the measured quality choice for broad intelligence, but GPT-5 mini (medium) is the practical value choice if its exact endpoint is available to you.

Claude 4.1 Opus (Reasoning) scores 33.7 on the Artificial Analysis Intelligence Index, compared with 30.9 for GPT-5 mini (medium). That result supports choosing Claude for workloads where general reasoning quality is the primary selection criterion. The evidence does not identify which tasks create that advantage, because the supplied official overview does not provide a model-specific benchmark or detailed capability boundary. Anthropic’s Claude models overview does not separately confirm the exact reasoning model name, slug, context window, maximum output, or dedicated parameters.

GPT-5 mini (medium) scores 85 on the Artificial Analysis Math Index, compared with 80.3 for Claude 4.1 Opus (Reasoning). That makes GPT-5 mini (medium) the better measured candidate for math-heavy evaluation sets, although the brief does not establish how well that result transfers to software repositories, tool use, structured extraction, or production agents.

The commercial gap is large in the supplied data: GPT-5 mini (medium) is listed at $0.6875 per 1M blended tokens, while Claude 4.1 Opus (Reasoning) is listed at $30. Yet OpenAI’s pricing page does not currently list the exact GPT-5 mini configuration. The lower number is therefore a benchmark-data advantage, not confirmed current vendor pricing for a directly callable endpoint.

The safest conclusion is conditional. Choose Claude when a confirmed Bedrock or Google Cloud route and higher broad intelligence score justify the cost. Choose GPT-5 mini (medium) only after confirming the exact model ID, endpoint, and current price in your account.

Claude 4.1 Opus (Reasoning) has the stronger broad intelligence result, while GPT-5 mini (medium) has the stronger measured math result.

The Artificial Analysis scores point to different strengths rather than a universal winner. Claude 4.1 Opus (Reasoning) reaches 33.7 on the Artificial Analysis Intelligence Index. GPT-5 mini (medium) reaches 30.9 on that index and 85 on the Artificial Analysis Math Index, versus 80.3 for Claude. A developer building a general coding assistant may therefore prefer Claude’s broad score, while a system dominated by mathematical transformations, symbolic work, or numerical reasoning may start with GPT-5 mini (medium).

The important limitation is transfer. The supplied data does not say whether the intelligence index measures repository-scale coding, debugging, planning, tool calling, or instruction following. It also does not explain the math index methodology. No reliable community reports were supplied for either exact model, so claims about coding feel, speed perception, model habits, or failure patterns would exceed the evidence.

Latency does not separate the models in the snapshot. Claude 4.1 Opus (Reasoning) and GPT-5 mini (medium) are both listed at 0.3 seconds. Median output tokens per second are unavailable for both models. This means the chart can support a latency tie, but it cannot support a streaming-throughput winner. Teams that care about interactive coding UX should measure time to first token, sustained output rate, tool-call overhead, and retry behavior in their own stack.

The official specifications are also incomplete. Anthropic’s Claude models overview does not confirm the exact Claude reasoning slug. OpenAI’s model directory does not list the exact GPT-5 mini configuration. Context-window and maximum-output values are unavailable for both in the supplied material. The evidence supports a targeted bake-off, not a confident claim about long-context or agent performance.

GPT-5 mini (medium) has the lower supplied token price, but Claude 4.1 Opus (Reasoning) has the clearer current pricing evidence.

The data snapshot lists GPT-5 mini (medium) at $0.6875 per 1M blended tokens, compared with $30 for Claude 4.1 Opus (Reasoning). It also lists GPT-5 mini (medium) at $0.25 per 1M input tokens and $2 per 1M output tokens. Claude is listed at $15 per 1M input tokens and $75 per 1M output tokens. Those figures make GPT-5 mini (medium) the obvious candidate for high-volume workloads if the exact configuration can be called at those rates.

The qualification matters because the current OpenAI pricing page does not list gpt-5-mini or gpt-5-mini-medium, so the supplied official material cannot confirm that price as current production pricing. The OpenAI Pricing page also does not confirm a stable alias for the configuration. A low benchmark price that cannot be reproduced at deployment time is not a realized saving.

Claude’s price is better documented. Anthropic’s Claude pricing lists Claude Opus 4.1 at $15 per 1M input tokens and $75 per 1M output tokens. The same page identifies the model as retired, with Bedrock and Google Cloud exceptions. That creates a different cost risk: the price is visible, but access is constrained and may depend on a particular cloud route.

Prompt caching changes the cost question for repeated context. Claude’s supplied pricing lists $18.75 per 1M tokens for a 5-minute cache write, $30 per 1M tokens for a 1-hour cache write, and $1.50 per 1M tokens for cache hits and refreshes. The brief provides no equivalent GPT-5 mini caching price. Developers should compare full request cost, cache behavior, provider fees, and migration work rather than selecting from the blended-token figure alone.

GPT-5 mini (medium) is the first model to test for cost-sensitive math workloads, while Claude 4.1 Opus (Reasoning) is the first model to test for broad reasoning quality.

Pick Claude 4.1 Opus (Reasoning) when your evaluation rewards broad intelligence, the higher 33.7 score matters, and your organization already has an approved Bedrock or Google Cloud path. Anthropic’s pricing documentation says Claude Opus 4.1 is retired except for those cloud exceptions, so a new dependency should include an explicit replacement plan. The supplied material does not identify the replacement model or provide a confirmed migration path.

Pick GPT-5 mini (medium) when math is central, the 85 math score is relevant to your workload, and the listed $0.25 input or $2 output price can be verified in your target account. The OpenAI materials do not currently confirm the exact model listing, stable alias, context window, or current price. Verification is therefore a release gate, not an administrative detail.

For coding agents, neither source set answers the most important operational questions. There is no reliable evidence here for repository navigation, patch quality, tool-call reliability, context retention, refusal behavior, or regression rate. Run the same task suite against both models, with identical prompts, tools, timeouts, and acceptance tests. Include math tasks, bug fixes, multi-file changes, structured output, and recovery after failed tool calls.

The final choice should be based on a three-part decision: measured quality, confirmed access, and observed task cost. The supplied snapshot gives useful quality and price signals. The vendor pages leave exact identity and lifecycle questions unresolved. A model that wins a chart but cannot be provisioned reliably is not the right production selection.

Claude 4.1 Opus (Reasoning) and GPT-5 mini (medium) both require endpoint verification before a production commitment.

The central evidence gap is model identity. Anthropic’s overview does not show the exact Claude reasoning slug, and OpenAI’s directory does not show the exact GPT-5 mini configuration. The supplied community research also contains no reliable, reproducible reports for either exact model. Those gaps make a small controlled evaluation more valuable than broad claims about developer experience.