Claude Opus 4.8 vs o3: Which Model Should Developers Choose?

Claude Opus 4.8 is the stronger default for developers who need broad reasoning and agent-oriented work, while o3 is the cheaper and faster option where math-focused capability and throughput matter more. Artificial Analysis reports an Intelligence Index of 55.7 for Claude Opus 4.8 and 30.4 for o3, while o3 has a Math Index of 88.3. The same dataset lists a blended price of $10 for Claude Opus 4.8 and $3.5 for o3 per 1M tokens. Data provided by https://artificialanalysis.ai/

Claude Opus 4.8 offers the clearer production story, but o3 offers the clearer efficiency story. Anthropic positions Claude Opus 4.8 for complex coding, agent workflows, and professional knowledge work, with text and image input, multilingual capability, and visual understanding. Anthropic’s release announcement Anthropic’s model overview

Decision factor	Claude Opus 4.8	o3
Broad intelligence evidence	Intelligence Index 55.7	Intelligence Index 30.4
Strongest supplied specialist evidence	Coding Index 74.3	Math Index 88.3
Blended price per 1M tokens	$10	$3.5
Median output speed	No supplied value	128.056 tokens per second
Reported latency	0.3 seconds	0.3 seconds
Current documentation evidence	Active model with lifecycle guidance	Current model page does not list o3

The comparison is asymmetric. Claude Opus 4.8 has a supplied coding score, while o3 has a supplied math score, so the materials do not establish a direct coding winner. OpenAI’s current model directory does not list o3, and its supplied documentation does not state an o3 context window, output limit, stable alias, or API endpoint. OpenAI’s model directory That documentation gap is itself a selection risk, not proof that o3 cannot be called.

Claude Opus 4.8 has the stronger supplied general-intelligence result, but o3 remains the only model here with a reported output-speed measurement. Artificial Analysis gives Claude Opus 4.8 an Intelligence Index of 55.7 versus 30.4 for o3. That gap supports choosing Claude for mixed workloads that combine planning, coding, explanation, and tool-oriented reasoning. Data provided by https://artificialanalysis.ai/

The practical meaning is narrower than a universal benchmark victory. The supplied dataset does not include a direct coding score for o3, so it cannot show whether Claude’s Coding Index of 74.3 translates into a coding advantage over o3. It also does not include a Math Index for Claude Opus 4.8, so o3’s Math Index of 88.3 cannot be treated as a general-purpose superiority claim.

Claude’s official feature set is more explicit. The model overview documents a 1M-token context window, a maximum synchronous output of 128k tokens, multimodal input, and Adaptive thinking. Anthropic’s model overview The effort documentation describes low, medium, high, max, and xhigh settings, but also says effort is a behavior signal rather than a strict token budget. Anthropic’s effort documentation

Community evidence complicates that advantage. One Reddit user reported better self-correction and improved control of answer length after three days of use, while also reporting that multi-step agents sometimes skipped explicit steps. Reddit field report A comment in the same thread reported a preference for the older version in a small non-coding A/B test because Adaptive thinking sometimes underestimates hidden subtask difficulty. Reddit discussion No comparable, testable community evidence was supplied for o3. Developers should therefore evaluate process compliance, not only final-answer quality.

o3 is the lower-cost choice by a wide margin, but Claude Opus 4.8 can justify its premium when a failed or weak agent run creates expensive human review. The supplied blended price is $3.5 for o3 versus $10 for Claude Opus 4.8 per 1M tokens. Input pricing is $2 versus $5, and output pricing is $8 versus $25. Data provided by https://artificialanalysis.ai/

The chart makes the nominal difference clear. The harder question is whether the cheaper model completes the whole workflow. A low unit price can become expensive if developers need more retries, more detailed prompts, additional verification calls, or more human intervention. The supplied evidence does not quantify retry rates, defect rates, or total task completion cost for either model, so no break-even point can be calculated.

Claude adds a configurable reasoning-quality trade-off through effort settings. Anthropic documents effort as a control for balancing capability and resource use, but explicitly warns that it is not a precise cost or latency ceiling. Anthropic’s effort documentation Prompt caching can also change repeated-context economics. Anthropic lists cache-hit pricing of $0.50 per 1M tokens, with separate write prices for five-minute and one-hour cache durations. Anthropic’s pricing documentation

For predictable high-volume workloads, o3 is easier to shortlist from the supplied numbers. For expensive engineering tasks, Claude’s higher intelligence evidence may reduce downstream correction work, but that remains a hypothesis because the brief supplies no controlled total-cost study. The identical reported latency of 0.3 seconds means the price decision should focus on workflow completion and output volume, not the supplied latency field.

Claude Opus 4.8 is the better primary pick for complex engineering workflows, while o3 is the better candidate for cost-sensitive math or high-throughput workloads with strong external validation. Anthropic explicitly targets Claude Opus 4.8 at complex coding, agent workflows, and professional knowledge work. Anthropic’s release announcement Its supplied Intelligence Index is 55.7, compared with 30.4 for o3. Data provided by https://artificialanalysis.ai/

Choose Claude Opus 4.8 when the task requires repository-scale reasoning, multimodal input, long context, or an agent that must recover from uncertainty. Keep a human review gate for plans, tool calls, migrations, and security-sensitive changes. The community report of skipped steps and unverified guesses means a correct final result does not prove a reliable process. Reddit field report

Choose o3 when the dominant constraint is token cost, the workload is math-heavy, or fast generated output has direct operational value. Its supplied Math Index is 88.3, its blended price is $3.5 per 1M tokens, and its median output speed is 128.056 tokens per second. Data provided by https://artificialanalysis.ai/ Treat availability as an unresolved deployment question. OpenAI’s current model directory does not list o3, and the provided official materials do not confirm its current endpoint, stable alias, or lifecycle status. OpenAI’s model directory

Run a short pilot with identical prompts, tool permissions, verification checks, and acceptance tests. Measure completed-task rate, correction effort, instruction adherence, and cost per accepted result. The supplied brief lacks those measurements, so production choice should remain conditional until the pilot fills the evidence gap.

Claude Opus 4.8 has more documented capabilities and lifecycle information, but the supplied evidence cannot establish a complete head-to-head winner. The materials provide Claude’s Coding Index of 74.3 but no corresponding coding value for o3. They provide o3’s Math Index of 88.3 but no corresponding math value for Claude. Only o3 has a supplied median output-speed result, while both models have reported latency of 0.3 seconds. Data provided by https://artificialanalysis.ai/

The documentation gap is larger for o3. OpenAI’s current models page does not list o3, and the supplied pricing page does not list an o3 price. OpenAI’s model directory OpenAI’s pricing documentation By contrast, Anthropic documents Claude Opus 4.8 pricing, effort controls, and lifecycle status. Anthropic’s pricing documentation Anthropic’s model lifecycle documentation

No reliable community benchmark or reproducible failure report was supplied for o3. Claude has useful but informal reports, including complaints about skipped steps and style drift. Claude Code Issue #77136 That imbalance should lower confidence in broad claims about o3, not automatically count as evidence against it.