o3 for research agents
o3 is ranked #4 on LLMDex's llms for research agents ranking out of 5 models we track for this use case. Below, the specific reasons it slots where it does, and when you should reach for an alternative.
Updated
At a glance
- Rank
- #4 of 5
- Context
- 200K tokens
- Output / 1M
- $8.00 / 1M tokens
- Released
- Apr 2025
Why o3 fits this task
Three things about o3 that map directly onto what this task rewards: Industry-leading reasoning depth at launch; Strong on math, science, and abstract puzzles; Tool-use during reasoning loops. Beyond the task-specific fit, o3 also brings industry-leading reasoning depth at launch and strong on math, science, and abstract puzzles, both of which compound when the workload broadens.
The criteria this task rewards
LLMDex ranks best llms for research agents on 5 criteria , these are the axes the ranking uses, in priority order:
- Web-search tool integration quality
- Source diversity and bias awareness
- Synthesis quality across many sources
- Citation discipline
- Long-context handling for retrieved corpus
How o3 scores on each axis
Where o3 costs you: slow first-token, unpredictable total latency. For most teams this is acceptable on this workload, the value of the strengths above outweighs the cost. For cost-bound workloads or teams with strict latency budgets, run an eval against the next two ranked models on real data before committing.
Strengths that pay off here
- Industry-leading reasoning depth at launch
- Strong on math, science, and abstract puzzles
- Tool-use during reasoning loops
Tracked weaknesses
- Slow first-token, unpredictable total latency
- Expensive when reasoning runs long
When to pick something else
If you can pay slightly more or accept slightly different tradeoffs, Gemini 3 Pro from Google ranks one position higher and tends to win on the hardest cases. Google's late-2025 flagship, set new benchmarks on long-context, vision, and reasoning at competitive pricing.
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Other models for research agents
- GPT-5.5 for research agents
OpenAI's mid-cycle GPT-5 refresh, improved reasoning, tool use, and multimodal grounding over the 2025 launch.
Read guide - Claude Opus 4.7 for research agents
Anthropic's mid-2026 flagship, ahead on SWE-bench, agent reliability, and writing quality.
Read guide - Gemini 3 Pro for research agents
Google's late-2025 flagship, set new benchmarks on long-context, vision, and reasoning at competitive pricing.
Read guide - Claude Sonnet 4.6 for research agents
Anthropic's mid-tier 4.6 release, the workhorse model behind most production Anthropic deployments.
Read guide
o3 for other use cases
Direct comparisons
Frequently asked
Is o3 good for research agents?
o3 is ranked #4 on LLMDex's research agents list. OpenAI's flagship reasoning model, set the bar for hard math, GPQA, and agent benchmarks in 2025.How much does o3 cost for research agents?
o3 costs $2.00 / 1M tokens for input tokens and $8.00 / 1M tokens for output tokens. For research agents workloads, output costs typically dominate; budget on the higher number.What's a cheaper alternative to o3 for research agents?
The next ranked model on this task is Claude Sonnet 4.6. Compare both before committing.When should I NOT use o3 for research agents?
Tracked weakness: Slow first-token, unpredictable total latency. If that constraint is binding for your workload, the next-ranked model on this task is the safer pick.
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