GPT-5 (high) vs GPT-5.6 Sol (high): The Ultimate Performance & Pricing Comparison

GPT-5.6 Sol (high) is the stronger default for demanding software work, while GPT-5 (high) remains the rational choice for cost-sensitive workloads. The Artificial Analysis snapshot gives GPT-5.6 Sol (high) a Coding Index of 77.2, compared with 37.8 for GPT-5 (high), and an Intelligence Index of 55.9, compared with 34.7. GPT-5 (high) costs $3.4375 per 1M blended tokens, versus $11.25 for GPT-5.6 Sol (high).\n\nThe naming also matters for implementation. Neither “GPT-5 (high)” nor “GPT-5.6 Sol (high)” is a separate high-suffixed API model ID. The official material describes high reasoning as a configuration applied to the model. See GPT-5 for developers, the GPT-5 model documentation, the GPT-5.6 Sol model page, and the reasoning models guide.\n\nData provided by https://artificialanalysis.ai/.

GPT-5.6 Sol (high) offers the clearer capability advantage, but GPT-5 (high) offers the clearer economic advantage. The coding score gap is large enough to matter for repository changes, multi-step debugging, and agent workflows. It does not prove that GPT-5.6 Sol (high) will finish every project better, because the supplied data does not include a shared task-level success rate.\n\n| Decision area | Better fit | Why it matters | |---|---|---| | Coding-heavy work | GPT-5.6 Sol (high) | Its Coding Index is 77.2 versus 37.8 | | Broad reasoning | GPT-5.6 Sol (high) | Its Intelligence Index is 55.9 versus 34.7 | | Math-specific evidence | GPT-5 (high) | The available Math Index is 94.3; no GPT-5.6 Sol value is supplied | | Blended token cost | GPT-5 (high) | $3.4375 versus $11.25 per 1M blended tokens | | Reported output speed | GPT-5.6 Sol (high) | 73.648 median output tokens per second; GPT-5 has no reported value | | Measured latency | Tie | Both models are listed at 0.3 seconds | \nGPT-5 remains attractive for applications where prompts are frequent, outputs are expensive, and tasks are bounded. GPT-5.6 Sol (high) becomes easier to justify when one successful complex run can replace several weaker iterations. The OpenAI model directory also gives GPT-5.6 Sol the more current product position, while the GPT-5 documentation marks its fixed snapshot as deprecated.

GPT-5.6 Sol (high) is the better performance choice for coding agents because its measured coding capability is substantially higher, even though the supplied latency values are identical. The Artificial Analysis Coding Index is 77.2 for GPT-5.6 Sol (high) and 37.8 for GPT-5 (high). That gap suggests a greater chance of handling interconnected changes, unfamiliar code, and reasoning-heavy implementation in fewer attempts. It does not establish a universal completion rate.\n\nThe practical distinction is between answer quality and interaction cost. GPT-5.6 Sol (high) reports 73.648 median output tokens per second, but the dataset has no comparable GPT-5 output-speed value. Both models list latency at 0.3 seconds, so the snapshot does not support claiming that one starts responding sooner. A developer should therefore treat GPT-5.6 Sol (high) as more capable, not automatically more responsive in every interface.\n\nThe official GPT-5.6 release announcement presents the newer model as suited to complex professional work, reasoning, and coding. The GPT-5 developer announcement positions GPT-5 around coding, reasoning, and agentic tasks. Community reports add caution: users describe GPT-5.6 Sol as slow or over-engineered in some workflows, while GPT-5 users report concise implementations and occasional incorrect changes. Those reports are subjective and not controlled comparisons.\n\nEvidence is insufficient to determine which model has the better high-effort success rate, average reasoning-token consumption, or production stability across a representative task set.

GPT-5 (high) is the clear cost winner, with a blended price of $3.4375 per 1M tokens compared with $11.25 for GPT-5.6 Sol (high). The input prices are $1.25 and $5, while output prices are $10 and $30. These differences make GPT-5 easier to deploy for high-volume classification, routine code assistance, short transformations, and user-facing features with strict margins.\n\nThe cheaper model can still become more expensive at the workflow level if it needs repeated retries, larger prompts, manual review, or extra orchestration. The available data does not measure retries or completed-task cost, so this is a selection principle rather than a demonstrated result. GPT-5.6 Sol (high) may justify its higher token price when stronger first-pass reasoning reduces downstream engineering time.\n\nLong-context usage introduces another cost boundary for GPT-5.6 Sol. The OpenAI pricing documentation states that requests beyond the documented threshold move to higher long-context pricing. The GPT-5.6 Sol model page documents the model’s large context capability, but the supplied comparison does not provide a matching GPT-5 context value. That makes long-document economics difficult to compare directly.\n\nUse the blended figure for an initial budget, then validate cost per completed task with production-like prompts. The price table alone cannot reveal whether the stronger model saves enough retries to offset its higher rate.

GPT-5.6 Sol (high) should be the default for complex coding agents, while GPT-5 (high) should serve cost-sensitive and bounded tasks. The newer model has the stronger Artificial Analysis Coding Index at 77.2 and Intelligence Index at 55.9. GPT-5 has the lower blended cost at $3.4375 and the only supplied Math Index, 94.3, so its weaker overall coding score does not erase its value for focused workloads.\n\nChoose GPT-5.6 Sol (high) when the model must inspect a large system, plan changes across files, use tools, or complete work where human review is expensive. Its official documentation lists structured outputs, function calling, file search, web search, and other agent-oriented tools. The GPT-5.6 Sol model page supports that capability profile.\n\nChoose GPT-5 (high) when the task is narrow, volume is high, or output tokens dominate the bill. Its official documentation supports coding, reasoning, structured outputs, function calling, and image input. The GPT-5 model documentation also shows a stable callable alias, but its fixed snapshot is marked deprecated and the page recommends a newer generation.\n\nA staged rollout is the safest selection method: route difficult tasks to GPT-5.6 Sol (high), keep GPT-5 (high) for predictable low-risk work, and compare completed-task cost. The supplied evidence does not identify a single winner for every developer workload, because no shared production benchmark, success rate, or review-cost measurement is available.

GPT-5.6 Sol (high) is the better first candidate for coding-agent evaluation because its Coding Index is 77.2 versus 37.8 for GPT-5 (high). The higher score does not guarantee better results for every repository, so evaluation should include the developer’s own tasks, tests, and review process.\n\nGPT-5 (high) is the better first candidate for strict token budgets because its blended price is $3.4375 versus $11.25 for GPT-5.6 Sol (high). That advantage matters most when tasks are frequent, outputs are long, and failures do not trigger expensive retries or manual intervention.\n\nGPT-5.6 Sol (high) is not proven to be faster to first response because both models list latency at 0.3 seconds. Its reported output speed is 73.648 median output tokens per second, but GPT-5 has no supplied comparable value.\n\nGPT-5 (high) has stronger available math evidence only because its Math Index is 94.3 and no GPT-5.6 Sol Math Index is supplied. The missing value prevents a fair math comparison, so developers should not interpret the available figure as proof that GPT-5 wins mathematical reasoning overall.