GPT-5 (high) vs Grok 4.3 (low): Which Model Should Developers Choose?

GPT-5 (high) is the safer documented choice, while Grok 4.3 (low) is the cheaper option with the stronger available general intelligence score.

The supplied data gives Grok 4.3 (low) an Artificial Analysis Intelligence Index score of 35.4, compared with 34.7 for GPT-5 (high). The same dataset lists Grok 4.3 (low) at $1.5625 per 1M blended tokens, compared with $3.4375 for GPT-5 (high).

That apparent Grok advantage does not settle a production decision. The research brief contains no verifiable official documentation for Grok 4.3 (low), including no confirmed API name, context window, output limit, parameters, modalities, pricing page, or official benchmark record. GPT-5 has documented developer controls and published evaluations, but its fixed snapshot is marked Deprecated in the supplied model documentation.

Data provided by https://artificialanalysis.ai/

GPT-5 (high) offers the stronger evidence base, while Grok 4.3 (low) offers the stronger measured value in the supplied dataset.

Decision factor	GPT-5 (high)	Grok 4.3 (low)
Artificial Analysis Intelligence Index	34.7	35.4
Artificial Analysis Coding Index	37.8	Not provided
Artificial Analysis Math Index	94.3	Not provided
Blended price per 1M tokens	$3.4375	$1.5625
Input price per 1M tokens	$1.25	$1.25
Output price per 1M tokens	$10	$2.5
Median output speed	Not provided	144.042 tokens per second
Latency	0.3 seconds	0.3 seconds

GPT-5 is explicitly positioned by OpenAI for coding, reasoning, and agentic tasks. Its documentation lists a 400,000-token context window, a maximum output of 128,000 tokens, text and image input, and text output. It also documents reasoning effort settings, verbosity settings, function calling, structured outputs, streaming, and custom tools. Sources: GPT-5 for developers and GPT-5 model documentation.

Grok 4.3 (low) cannot be assessed against those product capabilities from the supplied research. The model name, low reasoning setting, and data snapshot are available, but the underlying operational contract is not. Developers should therefore treat the numerical comparison as useful evidence about measured results and price, not as proof of equivalent API readiness.

GPT-5 (high) has the more useful performance evidence for engineering work, even though Grok 4.3 (low) leads the available general intelligence score.

The Artificial Analysis Intelligence Index gives Grok 4.3 (low) a score of 35.4 and GPT-5 (high) a score of 34.7. That result suggests a narrow measured edge for Grok on the available index, but it does not establish superiority across coding, mathematics, agents, or production workflows. The supplied comparison provides no Grok coding or math score, so no defensible winner can be declared in those areas.

GPT-5 has additional published evidence. OpenAI reports 74.9% on SWE-bench Verified, 88% on Aider polyglot, 96.7% on τ²-bench telecom, and 69.6% on Scale MultiChallenge. The SWE-bench result excludes 23 problems from 500 because they could not pass reliably on OpenAI’s infrastructure, and the Aider evaluation used high reasoning effort. Those qualifications matter because they limit how directly a developer should map the results to an application. Source: GPT-5 for developers.

The practical distinction is evidence depth. GPT-5 gives teams documented controls for adjusting reasoning effort and verbosity. That can support different latency, quality, and output-length policies across tasks. Grok 4.3 (low) has a supplied median output speed of 144.042 tokens per second, while both models have 0.3 seconds of listed latency. Speed may matter for interactive generation, but the research does not explain Grok’s endpoint behavior, token accounting, or quality at that speed.

The evidence is insufficient to conclude whether Grok 4.3 (low) is better for real code editing, long-running agents, or complex debugging. A controlled evaluation using representative repositories and tool traces remains necessary.

Grok 4.3 (low) is the clear price winner in the supplied data, but GPT-5 may still be cheaper for teams that value documented capability and predictable integration.

The blended price is $1.5625 per 1M tokens for Grok 4.3 (low) and $3.4375 for GPT-5 (high). Input pricing is identical at $1.25 per 1M tokens. The major difference is output pricing, listed as $2.5 for Grok 4.3 (low) and $10 for GPT-5. This makes Grok especially attractive for workloads that produce large responses, provided the model can meet the application’s quality and reliability requirements.

The cost conclusion can reverse when output quality changes the number of required attempts. A cheaper model that needs more retries, more validation, or more human correction can consume engineering time and operational capacity. The supplied research does not provide retry rates, failure rates, quality-adjusted cost, or production throughput for Grok 4.3 (low), so that risk cannot be quantified here.

GPT-5’s documented API surface may reduce discovery and integration work. OpenAI lists stable alias access through gpt-5, the fixed snapshot gpt-5-2025-08-07, Chat Completions, Responses, and Batch endpoints. The fixed snapshot is marked Deprecated, however, and the documentation recommends GPT-5.6. Source: GPT-5 model documentation.

For cost-sensitive prototypes, Grok 4.3 (low) deserves a direct trial. For a governed production system, price should be evaluated alongside availability, monitoring, migration risk, and task completion quality. The supplied evidence does not confirm whether Grok 4.3 (low) can be called directly or under what commercial terms.

GPT-5 (high) is the better default for documented coding and agent workflows, while Grok 4.3 (low) is the better candidate for low-cost experiments.

Choose GPT-5 (high) when the team needs a known API contract. OpenAI documents text and image inputs, text output, function calling, structured outputs, streaming, and custom tools. Custom tools can use developer-provided context-free grammars, which is relevant when an agent must produce constrained tool arguments. GPT-5 also exposes reasoning_effort values of minimal, low, medium, and high, plus verbosity values of low, medium, and high. Sources: GPT-5 for developers and GPT-5 model documentation.

Choose Grok 4.3 (low) when the main objective is to test lower response costs or faster generation. The supplied data lists a 35.4 Intelligence Index score, a $1.5625 blended price, and 144.042 median output tokens per second. Those figures justify a benchmark, not an unconditional production recommendation. The research brief provides no reliable Grok source confirming API access, tool support, context capacity, modalities, or pricing terms.

Do not select GPT-5 solely because its official benchmark list is longer. Do not select Grok solely because its available index score is higher. The decision depends on whether your workload rewards documented controls or lower measured cost.

A sensible evaluation should compare identical prompts, tool schemas, repository tasks, validation rules, and retry policies. Measure successful task completion, correction effort, response length, latency, and total spend. The supplied materials do not provide those application-specific results, so developers should record them before committing to either model.

GPT-5 also has an important lifecycle caveat. The stable gpt-5 alias remains listed, but gpt-5-2025-08-07 is marked Deprecated. Teams requiring snapshot stability should confirm the migration path before implementation.

GPT-5 (high) and Grok 4.3 (low) demand different validation questions because their available evidence is uneven.

The numerical data favors Grok 4.3 (low) on price, output speed, and the available general intelligence index. The research evidence favors GPT-5 on documented product behavior, published benchmarks, and developer controls. No supplied source closes the gap around Grok’s API availability or production contract.

Developers should resolve those unknowns before treating the comparison as a final architecture decision. The FAQ below separates supported conclusions from questions that require direct testing.