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

GPT-5.6 Sol (low) is the stronger documented choice, while GPT-5 nano (high) is dramatically cheaper but materially less certain to deploy.

The available evaluation data gives GPT-5.6 Sol (low) an Intelligence Index of 49.4, compared with 19.9 for GPT-5 nano (high). That gap makes Sol the safer candidate for tasks where broad reasoning quality matters. GPT-5 nano (high) has a Math Index of 83.7, but the comparison contains no matching Sol math score, so the evidence does not establish a math winner.

Cost points sharply in the opposite direction. GPT-5 nano (high) has a blended price of $0.1375 per 1M tokens, compared with $11.25 for GPT-5.6 Sol (low). The data snapshot reports the same latency, 0.3 seconds, for both models. Only Sol has a reported median output speed, at 69.917 tokens per second.

These results come from Artificial Analysis. OpenAI's current Models page lists the stable family alias gpt-5.6-sol, but the brief does not confirm gpt-5-6-sol-low as a separate callable identifier. The same page does not list GPT-5 nano. That availability uncertainty should shape any production decision.

GPT-5.6 Sol (low) is the better default for quality-sensitive development workflows, but GPT-5 nano (high) wins any decision dominated by token economics.

GPT-5.6 Sol (low) therefore fits applications that need a stronger general reasoning baseline, particularly where retries, manual review, or incorrect code would cost more than inference. The reported coding score supports that direction, although no nano coding score exists for a direct comparison.

GPT-5 nano (high) fits high-volume, cost-constrained workloads only when an evaluation harness can validate its actual behavior. Its low price is attractive for routing, classification, extraction, or other bounded tasks, but the supplied evidence does not confirm its current API alias, context window, output limit, or availability.

OpenAI describes the latest model family as supporting text and image inputs, text output, multilingual use, vision, Responses API access, and official SDKs on the Models page. The brief does not establish that every statement applies to either compared model individually.

GPT-5.6 Sol (low) offers the more complete performance case, while GPT-5 nano (high) has important measurement gaps that prevent a clean speed verdict.

The broad capability signal favors Sol. Its Intelligence Index is 49.4, versus 19.9 for nano, a reported difference of 29.5 points. For developers, that difference matters most when one response must combine requirements, code changes, edge-case handling, and explanation. A stronger general score can reduce the number of repair loops, but the brief does not provide retry rates, task-level accuracy, or production error data. The index should guide screening, not replace application tests.

Coding is another asymmetric comparison. Sol has a Coding Index of 69.7, while nano has no reported coding value. That does not prove nano is unsuitable for coding. It means the supplied evidence cannot quantify the coding trade-off. Teams choosing nano for code generation should test compilation, test repair, security-sensitive edits, and repository navigation directly.

Nano's Math Index is 83.7, but no matching Sol math score is available. A math-heavy workload could therefore produce a different ranking than the broad intelligence result. The evidence supports a capability split, not a universal winner.

Latency is reported as 0.3 seconds for both models, so Sol's quality advantage does not come with a documented latency penalty in this snapshot. Sol also reports 69.917 median output tokens per second. Nano's output speed is missing, so the data cannot show whether its lower price buys comparable interactive throughput. Artificial Analysis is the stated provider of these measurements.

GPT-5 nano (high) is the clear token-cost winner, but GPT-5.6 Sol (low) can be cheaper at the workflow level if it prevents expensive retries and review.

The blended price is $0.1375 per 1M tokens for nano and $11.25 for Sol. That is a large enough difference to dominate workloads with predictable, low-risk outputs and very high request volume. Nano is the natural first candidate for simple transformations, lightweight routing, and other tasks where a failed answer has a cheap recovery path.

Token price alone does not determine system cost. A cheaper model can lose its advantage if it needs more attempts, longer prompts, downstream validation, human review, or escalation to a stronger model. The supplied brief contains no retry rate, failure rate, token distribution, cache behavior, or end-to-end workload cost. Those missing values prevent a reliable total-cost-of-ownership conclusion.

Sol's reported input price is $5 per 1M tokens and output price is $30 per 1M tokens. Nano's corresponding values are $0.05 and $0.4. Output-heavy generation will expose the gap more quickly than short classification prompts. Long reasoning chains, tool calls, and repair loops can also make an apparently cheap model less economical.

A sensible production test should compare cost per accepted result, not cost per request. Measure successful outputs, retries, validator failures, escalation frequency, and human correction time on representative traffic. Treat the Artificial Analysis blended figures as a screening input rather than a final budget forecast.

Official pricing adds another risk. OpenAI's Pricing page lists gpt-5.6-sol, but the brief does not confirm a separate gpt-5-6-sol-low price. It also lists gpt-5.4-nano, not gpt-5-nano, so neither displayed official price should be silently mapped to the compared identifier.

GPT-5.6 Sol (low) should be the default quality choice, while GPT-5 nano (high) should be a measured optimization for bounded, high-volume tasks.

Choose GPT-5.6 Sol (low) when the application generates or reviews code, handles multi-step reasoning, or has a costly failure path. The available Coding Index of 69.7 and Intelligence Index of 49.4 provide the stronger evidence base. Sol also has a reported median output speed of 69.917 tokens per second and the same reported 0.3-second latency as nano. Those figures support interactive use, although the brief does not provide a direct nano throughput value.

Choose GPT-5 nano (high) when the task is narrow, the output is easy to validate, and volume makes inference cost a primary constraint. Its Math Index of 83.7 makes it worth testing for mathematical workloads, but the lack of a matching Sol math score prevents a head-to-head conclusion. Its $0.1375 blended price is compelling only if validation and retries remain inexpensive.

Use a two-tier router when the product contains both classes of work. Start bounded requests on nano, validate their outputs, and escalate ambiguous or failed cases to Sol. That architecture is reasonable, but the supplied evidence does not tell us the escalation rate or whether nano's actual API access remains available.

The largest decision risk is not the benchmark gap. It is identifier and availability uncertainty. The Models page does not independently confirm gpt-5-6-sol-low or gpt-5-nano as current standalone entries. Verify callable model IDs, quotas, context limits, output limits, and billing before committing to either integration.

GPT-5.6 Sol (low) and GPT-5 nano (high) both require direct API verification before a production commitment.

The supplied materials leave several operational questions unanswered. Neither model has a confirmed context window or maximum output limit in the brief. Neither compared identifier is clearly present as a standalone current entry in OpenAI's model directory. Community evidence is also absent for both models, so there is no reliable external record of coding quirks, speed perception, or recurring failure modes.

Developers should verify the exact model ID, pricing mapping, request parameters, rate limits, context behavior, and output behavior in the target account. They should then run representative tests that separate broad reasoning, coding, mathematics, latency, throughput, validation cost, and escalation frequency.

The FAQ below distinguishes supported conclusions from questions that remain evidence gaps.