GPT-5.5 vs o3
GPT-5.5 and o3 are both OpenAI reasoning models, but they serve different production decisions. GPT-5.5 is the 2026 frontier pick for long-context agentic coding, computer-use workflows, and newer internal knowledge. o3 is the cheaper, faster reasoning route when the task fits inside 200K tokens and does not require post-2024 knowledge.
Pick GPT-5.5 for large-codebase analysis, long-context agents, recent-knowledge research, computer-use automation, and coding accuracy: it has 1.05M context versus 200K, a December 2025 documented knowledge cutoff versus June 2024, GPQA Diamond at 93.6% versus 87.7%, and SWE-Bench Verified at 82.6% versus 71.7%. Pick o3 for cost-sensitive reasoning, math/science Q&A, batch jobs, and latency-sensitive flows under 200K tokens: OpenAI pricing is $2/M input and $8/M output versus GPT-5.5 at $5/M and $30/M, and Artificial Analysis reports materially faster output throughput for o3.
Decision scorecard
Local evidence first| Signal | GPT-5.5 | o3 |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 1.05m | 200k |
| Cheapest output | $30/1M tokens | $8/1M tokens |
| Provider routes | 3 tracked | 3 tracked |
| Shared benchmarks | SWE-bench Verified leader | 8 rows |
Decision tradeoffs
- GPT-5.5 holds a shared-benchmark lead on SWE-bench Verified, ahead by 10.9 points.
- GPT-5.5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags GPT-5.5 for Coding, RAG, and Agents.
- o3 holds a shared-benchmark lead on AIME 2025, ahead by 7.7 points.
- o3 has the lower cheapest tracked output price at $8/1M tokens.
- Local decision data tags o3 for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GPT-5.5
$11,500
Cheapest tracked route/tier: OpenAI API 0-272K input tokens
o3
$3,600
Cheapest tracked route/tier: OpenAI API
Estimated monthly gap: $7,900. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenAI API, OpenRouter, and Vercel AI Gateway; start route-level A/B tests there.
- o3 is $22/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Provider overlap exists on OpenAI API, OpenRouter, and Vercel AI Gateway; start route-level A/B tests there.
- GPT-5.5 is $22/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
Specs
Pricing and availability
| Pricing attribute | GPT-5.5 | o3 |
|---|---|---|
| Input price |
| $2/1M tokens |
| Output price |
| $8/1M tokens |
| Providers |
Capabilities
| Capability | GPT-5.5 | o3 |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | Yes |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | GPT-5.5 | o3 |
|---|---|---|
| SWE-bench Verified | 82.6 | 71.7 |
| Google-Proof Q&A | 93.6 | 87.7 |
| AIME 2025 | 81.2 | 88.9 |
| Humanity's Last Exam | 41.4 | 20.3 |
| HumanEval | 94.2 | 96.7 |
| Chatbot Arena | 1488.0 | 1412.0 |
| MMMU Pro | 88.3 | 76.4 |
| Aider Polyglot | 88.0 | 81.3 |
Deep dive
The first hard filter is context. o3's 200K-token window is large enough for many API tasks, but GPT-5.5's 1.05M-token window is the better fit for repository-scale review, long legal or technical document sets, and agents that need a large working memory. GPT-5.5 also has a long-context surcharge above 272K input tokens, so teams should estimate true session cost before treating the larger window as automatically cheaper.
Pricing strongly favors o3. The OpenAI rows track o3 at $2/M input, $8/M output, $0.50/M cached input, and batch pricing of $1/M input and $4/M output. GPT-5.5 tracks at $5/M input, $30/M output, $0.50/M cached input, and batch pricing of $2.50/M input and $15/M output. For high-volume async work, o3 can be several times cheaper before quality differences are considered.
The benchmark picture favors GPT-5.5, but the harness labels matter. GPQA Diamond is a cleaner head-to-head at 93.6% for GPT-5.5 versus 87.7% for o3. SWE-Bench Verified is directionally useful but not perfectly matched: GPT-5.5 uses the Vals.ai standardized 82.6% row, while o3 uses OpenAI's 71.7% custom agent scaffold row. GPT-5.5 has Terminal-Bench 2.0 and SWE-Bench Pro rows; o3 has no public rows for those variants in the handoff.
GDPval is intentionally not treated as a shared comparison row. GPT-5.5 has an official OpenAI GDPval percent score at 84.9%, while the existing GDPval-AA seed rows use an Elo-style Artificial Analysis variant. Those live under separate benchmark slugs so the page does not collapse non-comparable scores.
Knowledge freshness and latency can decide the pair even when benchmark direction is clear. GPT-5.5 is documented with a December 2025 cutoff, while o3 is documented at June 2024. The handoff also notes a community-reported GPT-5.5 cutoff inconsistency, so critical recency workflows should verify the exact API response. For streaming UX, o3's reported output speed and time-to-first-token are the reason to keep it in the shortlist.
FAQ
Which is cheaper, GPT-5.5 or o3?
o3 is cheaper on standard and batch token pricing. The tracked OpenAI row is $2/M input and $8/M output for o3 versus $5/M input and $30/M output for GPT-5.5. Batch pricing is $1/M input and $4/M output for o3 versus $2.50/M input and $15/M output for GPT-5.5.
When does GPT-5.5's larger context window matter?
GPT-5.5's 1.05M-token context matters when the workload needs full repositories, long transcripts, large research packs, or extended agent memory in one request. o3's 200K-token window is still enough for many prompts, documents, and API tasks, and it stays much cheaper when the larger window is not needed.
Which model is better for coding agents?
GPT-5.5 is the stronger first pick for coding agents in this pair. The seed uses GPT-5.5 at 82.6% on SWE-Bench Verified from Vals.ai and 82.7% on Terminal-Bench 2.0, while o3 has 71.7% on SWE-Bench Verified and no public Terminal-Bench 2.0 row in the researched sources.
Are the GPT-5.5 and o3 benchmark scores directly comparable?
Only some rows are directly comparable. GPQA Diamond is the cleanest shared benchmark. SWE-Bench Verified is directionally useful but has different harness labels. Terminal-Bench 2.0, SWE-Bench Pro, and OpenAI GDPval are asymmetric GPT-5.5 rows in this handoff, so the page should not invent o3 scores for them.
Is o3 still worth using in 2026?
Yes, when cost, speed, and a 200K-token ceiling fit the product. o3 remains a strong reasoning model with lower OpenAI pricing and faster reported output throughput. Its main tradeoffs against GPT-5.5 are the older June 2024 knowledge cutoff, smaller context window, and weaker or missing 2026 coding-agent benchmark rows.
Continue comparing
Last reviewed: 2026-06-08. Data sourced from public model cards and provider documentation.