Grok 4.5 (high)

Grok 4.5 is a strong developer model whose measured profile favors coding and agentic engineering, while its value depends on output-heavy workloads and careful context management.

The stable API identifier is grok-4.5. The word high describes the reasoning_effort parameter, not a separate model, and the official default is high in the developer guide. This distinction matters for routing, logging, and reproducibility.

The model accepts text and image input and returns text. It supports Responses API and Chat Completions, plus Function Calling, Structured Outputs, Web Search, X Search, and Code Execution, according to the model page and developer guide. The official announcement positions Grok 4.5 for coding, agentic tasks, engineering, and knowledge work.

Those interfaces make Grok 4.5 a credible candidate for repository agents, terminal workflows, structured extraction, and tool-based automation. The evidence does not establish a maximum output token limit, a systematic failure rate, or stable behavior across every provider. The model page leaves maximum output unspecified, so production selection should include a workload pilot.

Data provided by https://artificialanalysis.ai/; the Artificial Analysis homepage and provider record identify the data source used here.

Grok 4.5 offers a rare middle ground: leading measured capability with a lower blended price than every adjacent model in the snapshot.

The important selection signal is not a single benchmark win. Grok 4.5 stays in the leading group on both the broad intelligence and coding rankings, while its blended price sits below the nearby frontier alternatives listed in the snapshot. That combination makes it unusually easy to justify as a default candidate for teams that need strong code work without paying the highest model rates.

The tradeoff is less settled. The ranking data does not show whether Grok 4.5 produces fewer retries, better patches, or more reliable tool calls on your repositories. It also does not settle long-context cost, because the official pricing note says requests beyond the documented lower context threshold enter a higher tier whose exact amount is not stated on the model page.

Model	Tradeoff relative to Grok 4.5	Best reason to consider it
Grok 4.5	Lower blended cost with a leading coding placement	Balance coding ability and spend
GPT-5.6 Sol (medium)	Slightly better coding result, with a higher blended price	Pay more for a measured coding edge
Claude Opus 4.7	Slightly better coding result and no recorded output-speed value in the snapshot	Consider an alternative premium model
Claude Sonnet 5	Faster observed generation, slightly lower coding result, and a higher blended price	Prioritize response speed

This table is a choice map, not proof of universal superiority. The snapshot supports a strong price-quality case for Grok 4.5, but it does not resolve reliability, refusal behavior, or performance on a particular codebase.

Grok 4.5 is better suited to coding and tool-using workflows than a generic intelligence label would reveal.

The snapshot places Grok 4.5 at 15 of 202 on the Artificial Analysis Coding Index, with a score of 72.4. It also places the model at 13 of 578 on the Artificial Analysis Intelligence Index, with a score of 53.8. Those positions are close to the front of substantial comparison fields. For a developer, that is a shortlist signal: Grok 4.5 deserves evaluation for code generation, debugging, test repair, repository navigation, and agent loops.

The coding result should carry more weight for software work than the broad label. A high coding placement does not guarantee correct patches, clean diffs, or successful tests. It says the model has strong measured performance relative to the evaluated set. Your repository language, dependency graph, test quality, tool schema, and prompt conventions can change the outcome. The supplied material does not contain a controlled task breakdown that identifies where Grok 4.5 wins or fails.

Tool support strengthens the fit. The official documentation lists Function Calling, Structured Outputs, Web Search, X Search, and Code Execution, while the announcement targets coding and agentic work. See the developer guide and official announcement. Those capabilities are useful when the model must inspect state, call a tool, interpret the result, and produce the next action.

The service measurements suggest interactive use is plausible. The snapshot records a median output rate of 61.802 output tokens per second and latency of 0.3 seconds to first token. These are useful for estimating loop feel, but they are provider measurements, not a guarantee for every route. The Artificial Analysis provider record lists the recorded provider context. The evidence base also lacks reproducible community testing for long-term speed, coding style, and stability. A Reddit report describes an unusual coding-agent usage experience, but it does not disclose a repeatable test method. Treat it as a monitoring prompt, not a performance conclusion.

Grok 4.5 is a value pick only when its coding quality reduces retries, because long outputs and long contexts can erase the headline savings.

The snapshot gives Grok 4.5 a blended price of $3 per 1M tokens. That is below every adjacent model shown in the data. The input price is $2 per 1M tokens and the output price is $6 per 1M tokens. The practical point is not the displayed rate alone. Agentic coding often creates output through plans, tool arguments, patches, test interpretation, and retries. A model that needs fewer repair cycles can be cheaper at the same displayed rate, while verbose or uncertain behavior can make a lower blended price less attractive.

Caching deserves operational attention. The developer guide recommends setting prompt_cache_key; without it, a request may reach a cache-miss server and charge the full input rate. That makes cache configuration part of model economics, not a minor optimization. Teams should record cache hits, input tokens, output tokens, tool calls, and retry causes before making a cost decision.

Long context is another cost boundary. Grok 4.5 is documented with a 500,000-token context window, but requests beyond the documented lower context threshold move into a higher pricing tier. The official model page does not state the exact higher-tier amount. A large context window therefore proves capacity, not predictable affordability. Repository agents should compact history, remove stale tool output, and retrieve only relevant files.

Grok 4.5 is most compelling for workloads with meaningful coding value, moderate context reuse, and a need for many capable calls. It is less compelling when outputs are consistently long, context regularly crosses the higher tier, or a faster adjacent model reduces wall-clock delay enough to offset its higher price. Only trace-based testing can resolve that tradeoff.

Grok 4.5 is a sensible default for developers building code agents and structured tool pipelines under a controlled budget.

Use Grok 4.5 first when the product needs repository edits, terminal actions, structured tool calls, and broad engineering knowledge. Its coding placement and lower blended price make a strong initial case. The official announcement describes the target use cases, and the developer guide documents the interfaces needed to build them.

Use a different adjacent model when a specific tradeoff dominates:

Decision pressure	Recommendation
Coding quality matters more than spend	Test GPT-5.6 Sol (medium) or Claude Opus 4.7 because both show slightly stronger coding results in the snapshot.
Response speed is the main constraint	Test Claude Sonnet 5 because it has the fastest recorded output rate among the listed adjacent models.
Broad intelligence score matters most	Include GPT-5.5 (xhigh) in the pilot because its snapshot intelligence result is higher.
Budget and coding throughput both matter	Start with Grok 4.5, then compare completed tasks rather than isolated answers.

Do not choose based on the context window alone. Validate maximum output behavior because the model page does not provide it. Validate compaction and cache behavior for long agents. Validate routing and billing in the actual client because the Reddit report is anecdotal and disputed.

Overall, Grok 4.5 is worth piloting and may be the best cost-quality default in this snapshot for coding-centric agents. It is not proven as the best universal model. Evidence remains insufficient on maximum output, systematic failure rates, long-run stability, and task-level wins.

Grok 4.5 needs a production-shaped pilot because its rankings do not answer output limits, long-run agent stability, or routing behavior.

The pilot should compare complete task outcomes, not isolated replies. Capture patch acceptance, test results, tool-call validity, retry count, context compactions, cache behavior, and total tokens. Include short tasks and long repository sessions. Test normal prompts and difficult tasks that force structured outputs or code execution.

The evidence gap is explicit. The model page does not state maximum output tokens. The developer guide warns that long agent loops may need Context Compaction and that prompt caching affects billing. The Reddit report raises a routing and quota concern, but its account is not independently reproducible. These unknowns do not disqualify Grok 4.5. They define the checks required before production approval.