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

Grok 4.3 (low) is the stronger default choice in this dataset because it scores 35.4 on the Artificial Analysis Intelligence Index, produces 144.042 median output tokens per second, and costs $1.5625 per 1M blended tokens.\n\nThat conclusion is conditional, because the evidence does not establish whether Grok 4.3 (low) is directly callable, which API exposes it, or whether its label is stable. The same uncertainty affects o3 in a different way. OpenAI's current model directory does not list o3, and the supplied official material does not confirm a current endpoint or stable alias. Developers should therefore treat this as a measured-data comparison, not a production availability guarantee.\n\nThe dataset gives Grok 4.3 (low) the advantage on the broad intelligence index, output speed, and all listed price measures. It gives o3 the only supplied math result, 88.3 on the Artificial Analysis Math Index. No supplied result lets the comparison determine whether that math advantage generalizes to coding, tool use, long-context work, or production reliability. Data provided by Artificial Analysis.

Grok 4.3 (low) is the better measured-value option, while o3 remains a narrow candidate for workloads where its 88.3 math result matters most.\n\n| Decision factor | Grok 4.3 (low) | o3 | Selection meaning |\n|---|---:|---:|---|\n| Artificial Analysis Intelligence Index | 35.4 | 30.4 | Grok leads the supplied broad score |\n| Artificial Analysis Math Index | Not provided | 88.3 | o3 has the only supplied math result |\n| Median output speed | 144.042 tokens/s | 128.056 tokens/s | Grok has the higher measured throughput |\n| Latency | 0.3 seconds | 0.3 seconds | The supplied latency result is tied |\n| Blended price | $1.5625 | $3.5 | Grok has the lower blended price |\n\nThe important distinction is evidence coverage. Grok has no supplied math score, so the data cannot show whether it is weaker, comparable, or stronger on mathematical reasoning. o3 has no supplied broad evidence beyond the 30.4 Intelligence Index score, and no supplied coding or reliability evidence.\n\nAvailability creates a separate decision gate. OpenAI's current model directory lists newer model families but does not list o3 in the supplied research. The research also found no verified official documentation for Grok 4.3 (low). A team should confirm access, endpoint behavior, limits, and contractual terms before committing architecture or migration work.

Grok 4.3 (low) is the faster measured generator, but the available performance evidence does not prove better end-to-end application speed.\n\nThe supplied median output rate is 144.042 tokens per second for Grok 4.3 (low) and 128.056 for o3. That difference can matter in streaming interfaces, interactive coding assistants, and agents that spend much of their time waiting for generated text. It matters less when retrieval, tool calls, queueing, or post-processing dominate the request.\n\nThe supplied latency result is 0.3 seconds for each model. A developer should read this as no measured latency advantage in the provided snapshot. It does not establish tail latency, cold-start behavior, time to first token, connection overhead, or performance under concurrency. Those missing measurements can reverse the practical ranking for a production service.\n\nThe broad intelligence result favors Grok 4.3 (low), at 35.4 versus 30.4 for o3. The gap is evidence for the benchmarked index only. It does not identify which tasks created the difference, whether the scores are stable across prompts, or whether the result predicts a team's codebase and tool configuration.\n\nThe math evidence is asymmetric. o3 has an 88.3 Artificial Analysis Math Index score, while no Grok math value is supplied. That is a reason to test both models on the team's own mathematical tasks, not proof that o3 is the better general reasoning model. The research found no reliable community material to validate coding feel, speed perception, or recurring failure modes for either model.

Grok 4.3 (low) is the lower-cost option across every supplied token price, but token price alone cannot determine total application cost.\n\nThe blended comparison is $1.5625 for Grok 4.3 (low) versus $3.5 for o3 per 1M blended tokens. Input pricing is $1.25 versus $2, and output pricing is $2.5 versus $8. The output spread deserves attention for agents and coding workflows that generate long plans, patches, explanations, or tool arguments. A workload with unusually high output volume can feel the difference quickly.\n\nThe cheaper model can still be more expensive if it needs more retries, longer prompts, extra validation, or additional tool calls to reach an acceptable result. The supplied research contains no verified failure-rate, task-success, or retry data for either model. It therefore cannot convert the price chart into a reliable cost-per-successful-task estimate.\n\nThe reverse risk also exists. o3's supplied 88.3 math score may justify its higher price for a narrowly defined workload if it reduces human review or downstream correction. The evidence does not show that it does so. Developers should measure completed-task cost, not only token cost, using representative prompts and identical evaluation rules.\n\nOpenAI's current pricing page does not list o3 in the supplied research, so the $3.5 figure should be treated as dataset evidence rather than a confirmed current OpenAI price. Grok 4.3 (low) also lacks a verified official pricing page in the research.

Grok 4.3 (low) is the recommended first candidate for cost-sensitive, latency-aware applications that can verify access independently.\n\nChoose Grok 4.3 (low) for a broad default evaluation when the priority is measured intelligence per token, faster generated output, or lower output cost. Its supplied 35.4 Intelligence Index score exceeds o3's 30.4, and its 144.042 median output rate exceeds 128.056. These results support a practical pilot, not an unconditional production recommendation, because the research has no verified official documentation or community test record for this model.\n\nChoose o3 when mathematical reasoning is a central acceptance criterion and the 88.3 Math Index result aligns with your task. Keep the scope narrow until access is confirmed. OpenAI's current model documentation does not list o3 in the supplied research, and the research does not confirm a stable alias, endpoint, context window, output limit, or successor relationship.\n\nA sensible selection sequence is simple: verify that each model can be called through the intended provider, run the same private task set, record successful completion rather than only raw scores, then compare token cost and review time. The supplied evidence cannot answer which model is safer for coding, tool calling, long prompts, multimodal input, or sustained production traffic. Those are the highest-priority gaps for a developer evaluation.\n\nDo not infer that equal 0.3-second latency means equal user experience. Streaming behavior and generation length may still differ. Do not infer that o3's math result proves a general advantage. The supplied broad score points in the other direction.

o3 is the model with the clearest named official vendor context, but its current API status remains unresolved in the supplied research.\n\nThe main pre-commit question is not which row wins the chart. It is whether the rows describe models that the team can access reliably under the intended terms. The research provides no verified official source for Grok 4.3 (low), while OpenAI's current directory and pricing page do not list o3 in the supplied material.\n\nThe next question is task fit. The data supports Grok 4.3 (low) on the supplied Intelligence Index and speed measures. It supports o3 on the supplied Math Index. It does not support a conclusion about coding, tool calls, multimodal work, context limits, output limits, failure modes, or community experience.\n\nTeams should also ask how much evidence is enough for their risk level. A low-stakes prototype can start with the measured cost and speed advantage. A financial, scientific, or developer-critical workflow needs an acceptance suite that tests correctness, retries, review burden, and operational access. None of those results appears in the supplied brief, so the article cannot responsibly fill the gap with assumptions.\n\nData attribution: Data provided by Artificial Analysis.