Grok 4.3 (high) vs o3: The Ultimate Performance & Pricing Comparison

Grok 4.3 (high) is the stronger default for measured general capability and price, while o3 remains the clearer specialist choice for math-heavy work. The available Artificial Analysis data gives Grok 4.3 (high) an Intelligence Index of 37.6, compared with 30.4 for o3, and lists a blended price of $1.5625 versus $3.5 per 1M tokens. Data provided by https://artificialanalysis.ai/

The comparison has an important operational limitation. The supplied research found no verifiable vendor announcement, developer documentation, or pricing page for Grok 4.3 (high). For o3, the current OpenAI model directory does not list the model, and the current pricing page does not list an o3 price. OpenAI Models and OpenAI API Pricing therefore provide evidence about current visibility, not a confirmed live endpoint for o3.

Developers should treat the benchmark result and the procurement decision as separate questions. Grok 4.3 (high) looks better on the available aggregate score and cost data. Neither model has a confirmed context window in the supplied evidence. That missing information can dominate production suitability for long documents, code repositories, retrieval payloads, and agent traces.

Grok 4.3 (high) offers the better measured value proposition, but o3 has the only reported math result and the only reported output-speed result. Data provided by https://artificialanalysis.ai/

The table does not establish a universal winner. It establishes an evidence asymmetry. Grok 4.3 (high) has a stronger aggregate intelligence score, a reported coding score, and lower listed prices. o3 has a strong reported math score and measured output speed. The missing values prevent a clean claim about coding parity, Grok 4.3 (high) generation speed, or end-to-end production behavior.

OpenAI’s current directory lists GPT-5.6 Sol, GPT-5.6 Terra, and GPT-5.6 Luna as the latest frontier models, while the supplied page does not list o3. OpenAI Models That status makes model lifecycle verification part of the selection process. The research brief also found no reliable community material for either model’s coding feel, speed perception, behavioral preferences, limitations, or failure modes.

o3 is the safer choice for math-focused evaluation, while Grok 4.3 (high) is the stronger measured choice for broad capability, but the performance evidence is incomplete. Data provided by https://artificialanalysis.ai/

The available scores point in different directions. Grok 4.3 (high) records an Artificial Analysis Intelligence Index of 37.6, compared with 30.4 for o3. That gap supports choosing Grok 4.3 (high) for mixed workloads where broad reasoning quality is the main measured objective. It does not prove that Grok 4.3 (high) wins every task, because the aggregate index does not expose the individual task distribution in the supplied brief.

o3 records an Artificial Analysis Math Index of 88.3, while no corresponding Grok 4.3 (high) value is available. That makes o3 the only evidence-backed option for a math-specific preference. The result should still be validated against the developer’s own problems, especially if correctness depends on symbolic manipulation, multi-step verification, tool calls, or strict output formats.

Coding evidence is similarly one-sided. Grok 4.3 (high) has a Coding Index of 42.2, while o3 has no reported value. The available material therefore cannot establish a coding winner. A team selecting for repository changes, debugging, tests, or code review should run a task set that measures patch correctness and regression rate, rather than infer those outcomes from the general intelligence score.

Speed evidence favors caution. o3 reports 128.056 median output tokens per second, but Grok 4.3 (high) has no reported output-speed value. Both models show 0.3 seconds of reported latency, yet latency alone does not reveal time to first token, sustained generation behavior, queueing, retries, or tool-call overhead. The evidence is sufficient to say that o3 has documented throughput in this snapshot. It is insufficient to say that o3 feels faster in a complete application.

Grok 4.3 (high) is the lower-cost option on every supplied token-price measure, but o3 can still be economically rational when its stronger task fit reduces rework. Data provided by https://artificialanalysis.ai/

The blended comparison favors Grok 4.3 (high) at $1.5625 per 1M tokens versus $3.5 for o3. The input difference matters for applications that send large prompts, repository context, retrieved documents, or repeated conversation history. The output difference matters even more for verbose agents, because o3 is listed at $8 per 1M output tokens compared with $2.5 for Grok 4.3 (high).

Those prices do not determine total workload cost by themselves. A lower token price can become the more expensive choice if the model produces more failed patches, requires more retries, needs additional verification calls, or cannot reliably complete the task in one pass. The supplied research brief contains no verified failure-rate, retry-rate, or task-success data for either model. Any total-cost claim beyond token pricing would therefore be unsupported.

The cost conclusion can also flip according to workload shape. Prompt-heavy systems should focus on input pricing and context retention. Agent systems should inspect output volume, tool-call frequency, and recovery behavior. Math-heavy systems should compare the cost of a correct o3 response with the cost of repeated attempts from a less suitable model. The available data shows o3’s Math Index at 88.3 and Grok 4.3 (high)’s blended price at $1.5625, but it does not measure the relationship between those facts and production success.

Procurement risk is part of cost. The supplied research could not confirm a current Grok 4.3 (high) endpoint or stable alias. OpenAI’s current model and pricing pages also do not list o3. OpenAI Models OpenAI API Pricing A model that cannot be reliably invoked has an operational cost that token tables cannot capture.

Grok 4.3 (high) is the better first candidate for general development workloads, while o3 deserves a targeted trial for math-intensive tasks. Data provided by https://artificialanalysis.ai/

Choose Grok 4.3 (high) first when the workload mixes ordinary reasoning, coding assistance, and cost-sensitive generation. Its Intelligence Index is 37.6, its Coding Index is 42.2, and its blended token price is $1.5625. The recommendation is based on the supplied measurements, not on a verified public feature set. The research brief found no reliable official or community evidence confirming its context window, output limit, API parameters, multimodal support, stable alias, or current availability.

Choose o3 for a controlled math evaluation when mathematical accuracy is central and the team can verify access. o3 is the only model with a supplied Math Index, at 88.3, and the only model with a supplied median output speed, at 128.056 tokens per second. Those facts justify a focused trial. They do not establish that o3 is currently available, supported, or preferable for general coding. The current OpenAI model directory does not list o3. OpenAI Models

Do not make a final production decision from the aggregate score alone. First confirm that the selected model has a usable endpoint, stable identifier, documented context behavior, and acceptable output limits. Then test representative prompts for code changes, debugging, math, structured output, and long-context tasks. The supplied research does not answer those operational questions for either model.

The practical decision rule is simple. Start with Grok 4.3 (high) for broad, budget-sensitive development work. Add o3 when math correctness is the bottleneck. Keep the decision provisional until availability, context behavior, and failure rates are verified in the intended serving environment.

Developers should resolve availability and workload-fit questions before treating the benchmark comparison as a deployment decision. OpenAI Models Data provided by https://artificialanalysis.ai/