Grok 4.5 vs GPT-5.6 Sol: Which Flagship Model Should You Use?

Grok 4.5 and GPT-5.6 Sol are both frontier reasoning models, but they are not interchangeable buying choices. Grok 4.5 is the stronger default when low token prices, native X search, and a compact coding-agent stack matter most. GPT-5.6 Sol is the stronger default when the workload needs a larger context window, a published 128,000-token output ceiling, finer reasoning controls, or OpenAI’s broader native-tool surface. Neither is a universal winner: quality-sensitive teams should make the final call with a matched workload evaluation.

This guide compares the exact API models, not the surrounding product families. The OpenAI side is GPT-5.6 Sol—not Terra, Luna, Max, or Ultra—and the xAI side is Grok 4.5. Readers choosing an end-user subscription should use the Grok product page or ChatGPT product page. Readers separating Grok app access from API billing should continue to Grok pricing and Grok subscription versus xAI API pricing.

Decision summary

The recommendation changes with workload shape. Grok 4.5’s price advantage can dominate a high-volume pipeline, especially when responses are long. Sol’s larger working envelope and execution controls can justify its premium when a task would otherwise need chunking, external orchestration, or repeated recovery passes.

API identity, context, and output limits

Use the exact IDs when model-tier identity matters. The unsuffixed gpt-5.6 alias currently routes to Sol, but it is still a family alias; code and telemetry should record the resolved model. xAI’s latest aliases are explicitly moving targets. Neither public model card currently shows a dated immutable snapshot, so regulated or reproducibility-sensitive deployments should confirm snapshot availability and alias policy with the vendor.

The limit comparison is not simply “one million versus half a million.” If a Sol request reserves the full 128,000-token output maximum, subtracting it from the 1,050,000-token context leaves about 922,000 tokens for input. That is an arithmetic planning budget, not a separately published input-limit field. xAI publishes 500,000 tokens as Grok 4.5’s context window but does not expose a separate maximum-output figure on the public card. Do not infer that the full window is available for either input or output; verify the effective cap with the target endpoint and account.

Token price, cache, and long-context economics

Standard synchronous token prices per one million tokens are:

Both vendors sharply discount reusable prompt prefixes, but the write economics differ. xAI publishes uncached and cached-input rates and recommends a prompt cache key in Responses or a conversation-affinity header in Chat Completions. OpenAI lets GPT-5.6 callers choose implicit or explicit caching and bills a cache write at 1.25 times the uncached input rate.

For one million eligible prefix tokens followed by one complete cache hit, the input-only arithmetic is $2.50 on Grok 4.5 and $6.75 on Sol. Without caching, the same two reads would be $4 and $10. This illustration excludes output, reasoning, tool calls, misses, minimum cacheable prefixes, and expiry. A cache write that is never reused can cost more on Sol, so track cache-write and cached-token telemetry rather than assuming every cache entry saves money.

Long prompts change the comparison again. xAI’s Grok 4.5 model data sets a 200,000-token long-context boundary and publishes $4 input, $1 cached input, and $12 output beyond it. Sol applies long-context pricing to the full request when input exceeds 272,000 tokens: $10 input, $1 cached read, $12.50 cache write, and $45 output. Cached reads converge to $1, but first-pass input and especially output remain much cheaper on Grok. OpenAI’s Batch and Flex rates can halve Sol token prices when their latency and processing contracts fit; xAI does not currently publish an equivalent Grok 4.5 batch discount.

Native tools add separate meters. xAI lists web search, X search, and code execution at $5 per 1,000 calls, with model tokens billed as well. OpenAI lists web search at $10 per 1,000 calls plus search-content tokens at the selected model rate; containers, file search, and other hosted tools have their own meters. Estimate a complete task, not just the headline token row.

Reasoning and execution controls

Grok 4.5 exposes low, medium, and high reasoning effort, defaults to high, and does not let callers disable reasoning. xAI can return a reasoning summary, while Responses integrations can carry encrypted reasoning content. This smaller control surface is straightforward, but it gives latency-sensitive applications no true none setting.

GPT-5.6 Sol exposes none, low, medium, high, xhigh, and max, with medium as the default when effort is omitted. Pro is an execution mode on the same model, controlled independently from effort; it is not a separate model slug. It spends more model work to improve reliability and can materially increase latency and billed usage.

Ultra is also not a Sol model ID or an API reasoning value. It is a product-level multi-agent mode. The closest API feature is OpenAI’s Responses multi-agent beta, where one GPT-5.6 instance coordinates subagents. Compare that orchestration separately from single-agent Sol, and do not attribute an Ultra benchmark to a normal gpt-5.6-sol call.

For a fair migration, start at a reasoning effort shared by both models—low, medium, or high—and compare quality, latency, and token use. Only then test Sol-specific none, xhigh, max, or pro modes. Otherwise an apparent model gain may really be an effort-budget gain.

Structured outputs and native tools

Both models support function calling and schema-constrained output, but implementation compatibility is not guaranteed. xAI documents JSON Schema response formats, JSON-object mode, and strict function arguments over a supported schema subset. Sol’s model card lists structured outputs and function calling, and OpenAI recommends Responses for reasoning, multi-turn state, and hosted tools. Validate every production schema against both vendors rather than assuming that a schema accepted by one will be accepted by the other.

Grok 4.5 has the distinctive social-research route. X search can filter by handles and dates and can inspect image or video posts, while web search covers the broader internet. That makes Grok the more direct choice when X is a required first-party corpus rather than merely one source among many.

Sol has the broader execution palette. Programmatic Tool Calling can run bounded JavaScript that coordinates eligible tools and compresses intermediate results, while hosted shell, patching, skills, computer use, and remote MCP cover more agent architectures. This breadth also increases governance work: each tool has permissions, billing, state, and retention behavior that must be tested separately.

Coding and agentic evidence

Vendor-reported evidence

xAI positions Grok 4.5 for coding, agentic tasks, and knowledge work. Its launch material reports 83.3 on Terminal-Bench 2.1, 64.7 on SWE-Bench Pro, 62.0 on DeepSWE 1.0, and 53 on DeepSWE 1.1. xAI also reports roughly 80 generated tokens per second and emphasizes lower output-token use in its coding runs.

OpenAI’s GPT-5.6 launch reports 88.8 on Terminal-Bench 2.1, 64.6 on SWE-Bench Pro, and 72.7 on DeepSWE 1.1 for Sol. It also reports 80 on an Artificial Analysis Coding Agent Index configuration. OpenAI’s Ultra results use multiple agents, and its published tables include model- and harness-specific footnotes.

These rows should not be turned into a normalized score or a single winner. The vendors did not use one shared harness, reasoning budget, tool set, retry policy, or agent product. Even similarly named benchmarks can have different versions and execution settings. Treat each table as evidence for the vendor’s positioning, then reproduce the workload conditions that matter to your application.

Independent evidence

Artificial Analysis reports Grok 4.5 at high effort scoring 54 on its Intelligence Index, with about 89.5 output tokens per second in its measured provider configuration. Its launch analysis reports 76 on the Coding Agent Index with Grok Build. For GPT-5.6 Sol, Artificial Analysis reports 54 at medium effort and 59 at max effort on the Intelligence Index, plus 80 on the Coding Agent Index with the Codex harness.

This independent layer is useful because it applies a published external methodology, but it still does not isolate model weights. The Grok coding run uses Grok Build; the Sol coding run uses Codex. The 54-to-54 intelligence result compares Grok high with Sol medium, while Sol max spends a different reasoning budget. Read these as configuration evidence, not a clean head-to-head ranking.

No hands-on ToolColumn result is represented here. A publishable local conclusion would require an explicitly supplied evaluation set, raw run logs, scoring method, and reviewer sign-off.

Latency, throughput, and service tiers

xAI’s roughly 80-tokens-per-second launch claim is directionally consistent with Artificial Analysis’s approximately 89.5-token-per-second observation, but both are configuration-dependent. Prompt length, reasoning effort, tool calls, region, load, and output shape can dominate end-to-end latency. Neither number guarantees production throughput or time to first token.

OpenAI does not publish one general Sol tokens-per-second guarantee on the model card. Its announcement of up to 750 tokens per second on Cerebras applies to select customers and specialized access, not the ordinary API baseline. Priority processing charges higher token rates for more consistent latency; Batch and Flex trade immediacy for lower cost. Multi-agent execution can reduce wall-clock time on work that splits cleanly, but it is not a raw generation-speed claim.

Measure time to first token, generation rate, total wall-clock time, tool wait, retries, and tail latency independently. A cheaper, faster stream can still lose if it needs more recovery turns; a slower high-effort call can win if it completes a task reliably in one pass.

Data retention and governance

Both vendors state that API inputs and outputs are not used to train their models unless the customer explicitly opts in or grants permission. Their default retention boundary is not zero, however.

xAI states that API request and response data is retained for 30 days by default for abuse and misuse auditing. Zero Data Retention is an enterprise control. Under ZDR, server-side continuation features can change, so applications may need to carry conversation and encrypted reasoning state themselves.

OpenAI retains abuse-monitoring logs for up to 30 days by default. Responses application state is also stored for at least 30 days when default storage is used; approved ZDR organizations force store to false. Prompt-cache tensors can remain in GPU-local storage for up to 24 hours, and the GPT-5.6 cache TTL controls a minimum lifetime rather than that maximum. Hosted containers, files, background mode, conversations, web search, and MCP each have additional state or third-party boundaries.

Do not select on a “no training” statement alone. Confirm the exact endpoint, state flag, cache mode, search mode, hosted tool, region, and organization-level retention approval. Any data sent to external MCP servers or other third-party services follows those services’ policies as well.

Migration effort

A safe migration starts with an inventory of prompts, schemas, tools, state dependencies, cache keys, rate limits, and compliance assumptions. Freeze a representative test set, run the existing model and candidate at the same shared effort, then compare one setting at a time. Include adversarial tool failures and malformed structured outputs, not only happy-path answers.

Canary the candidate behind a reversible route. Log resolved model ID, effort and mode, tool calls, token categories, cache reads and writes, latency, retries, and task outcome. Keep the old provider path available until quality and cost remain stable under real traffic.

Final workload boundary

Choose Grok 4.5 first when X-native research is a hard requirement, when standard and long-output token economics dominate, or when a 500,000-token working window is sufficient. Its low input and output rates make it attractive for large coding and agent loops, but the public output cap and production snapshot policy still need confirmation.

Choose GPT-5.6 Sol first when the workload needs more than 500,000 tokens of context, a documented 128,000-token output ceiling, the broadest hosted execution tools, or reasoning controls beyond low, medium, and high. Its premium is easiest to justify when those capabilities eliminate application-side orchestration or improve completion reliability enough to offset higher token and cache-write costs.

For serious coding, agentic, or regulated workloads, the final answer should remain conditional until matched evaluations and governance checks pass. If Sol is the likely route, compare it only with its sibling tiers in the GPT-5.6 Sol, Terra, and Luna guide, then use the GPT-5.6 Max versus Ultra guide to separate product execution modes from the underlying API model.