Executive Summary
GPT-5.5 "Spud" is the model you pick when you do not know exactly what your workload looks like. It rarely wins any individual category outright but it never loses badly on any category. That is what the leading AAII score measures and what makes it the safest default for general-purpose production traffic. The Pro variant exists for one reason: to capture the customers who need the absolute strongest reasoning at any price.
Three strengths
- Highest AAII (59). The most reliable generalist in the market.
- Tool-use reliability leader. Best schema compliance under unusual function signatures, lowest tool error rate in long agent loops.
- Long-form drafting and structured output. Wins N1 (long-form) and N8 (JSON schema) outright.
Three weaknesses
- Pricing. $30 output is the highest among non-Pro frontier; $180 Pro output sets a new market ceiling.
- Multi-file refactor. Loses to Claude Opus 4.7 by a meaningful margin on code-spanning tasks.
- Hallucination on N9 (domain QA). 5.1% fabrication rate, highest of the frontier four. The model will confidently invent citations under retrieval pressure.
Architecture and Training
- Mixture-of-experts. Active parameter count not disclosed; community estimates put it in the 100-200B active range from a much larger total. The "Spud" codename references the project's internal staging.
- Two variants. GPT-5.5 standard and GPT-5.5 Pro share the base weights; Pro runs with a much larger thinking budget and additional verifier passes.
- Tokenizer. Carries forward the GPT-4 family tokenizer (cl100k variant). No drift to manage when upgrading from GPT-4o or GPT-5.
- Knowledge cutoff February 2026. Tied for the freshest among closed-frontier models.
Pricing Reality
| Tier | Input ($/1M) | Output ($/1M) | Notes |
|---|---|---|---|
| GPT-5.5 standard | $5.00 | $30.00 | Default for most workloads |
| GPT-5.5 cached input | $1.25 | $30.00 | 4x cheaper than uncached input |
| GPT-5.5 batch | $2.50 | $15.00 | 50% off, async only |
| GPT-5.5 Pro | $30.00 | $180.00 | 6x standard input, 6x standard output |
| GPT-5.5 Pro cached | $7.50 | $180.00 | 4x cheaper than uncached Pro input |
Pro tier reality check. The $180 output rate compounds with reasoning models' chain-of-thought billing at the output rate. A single Pro answer to a hard reasoning prompt (including the thinking trace) can run $0.50-$2 per call. For a 50K/month workload, the bill is $25K-$100K monthly. The Pro variant is not a default; it is a per-call choice for the hardest 1-3% of traffic.
SMQTS Results: Programming Series
| Category | GPT-5.5 | Opus 4.7 | Gemini 3.1 Pro | DeepSeek V4 Pro |
|---|---|---|---|---|
| P1 Multi-file refactor | 86 | 94 | 83 | 74 |
| P2 Bug-finding from stack trace | 87 | 92 | 84 | 78 |
| P3 Code review | 88 | 91 | 85 | 76 |
| P4 Test generation | 90 | 89 | 83 | 77 |
| P5 SQL from natural language | 89 | 87 | 91 | 82 |
| P6 Algorithm from spec | 89 | 93 | 88 | 79 |
| P7 Migration scripts | 83 | 92 | 80 | 71 |
| P8 Documentation | 88 | 90 | 85 | 78 |
| P9 Diff comprehension | 86 | 91 | 83 | 76 |
| P10 Tool-using agent loops | 92 | 89 | 85 | 74 |
| Average | 87.6 | 91.2 | 84.6 | 76.5 |
GPT-5.5 wins P4 (test generation) and P10 (tool-using agent loops) outright. On P1, P2, P3, P6, P7, P8, P9 it loses to Opus 4.7. On P5 it loses to Gemini. Solid second place.
SMQTS Results: Non-Programming Series
| Category | GPT-5.5 | Opus 4.7 | Gemini 3.1 Pro |
|---|---|---|---|
| N1 Long-form drafting | 91 | 87 | 89 |
| N2 Summarization | 89 | 91 | 90 |
| N3 Multi-step reasoning | 88 | 83 | 94 |
| N4 Information extraction | 88 | 89 | 87 |
| N5 Translation | 84 | 76 | 92 |
| N6 Style transfer | 89 | 90 | 87 |
| N7 Adversarial resistance | 85 | 92 | 88 |
| N8 Structured output | 91 | 87 | 88 |
| N9 Domain QA | 87 | 90 | 89 |
| N10 Multi-turn coherence | 87 | 91 | 89 |
| Average | 87.9 | 87.6 | 89.3 |
AAII headline (composite)
GPT-5.5 59.0 ############################## Gemini 3.1 58.4 ############################# Opus 4.7 58.1 ############################# DeepSeek V4 54.7 ########################### Gemma 4 27B 47.2 #######################
SMQTS Results: Cost-Quality Validation
Pairwise blind grading of GPT-5.5 standard vs cheaper tier substitutes on the 50-prompt sample:
| Workload | GPT-5.5 wins | DeepSeek V4 Pro wins | Tie |
|---|---|---|---|
| Long-form drafting (N1) | 56% | 21% | 23% |
| Tool-using agent loops (P10) | 67% | 13% | 20% |
| Information extraction (N4) | 22% | 34% | 44% |
| Structured JSON output (N8) | 41% | 28% | 31% |
GPT-5.5 Pro vs GPT-5.5 standard, on the hardest reasoning sub-set of N3:
| Workload | Pro wins | Standard wins | Tie |
|---|---|---|---|
| N3 Hard reasoning subset | 61% | 14% | 25% |
| P6 Algorithm from spec (hard) | 43% | 22% | 35% |
| P10 Tool loops (10+ turn) | 34% | 30% | 36% |
Procurement reading. Pro pays for itself only on the hardest reasoning subset, and the gap shrinks fast as prompts get easier. For a workload routing 95% to standard and 5% to Pro, the blended cost is roughly $13/$45 per 1M: still expensive, but defensible if reasoning quality is critical.
Strengths in Detail
Tool-use reliability
GPT-5.5 produces valid tool calls on first attempt 97.4% of the time across our P10 prompts, including unusual variadic and deeply nested function schemas. The next-best (Opus 4.7) hits 94.2%. For high-volume agentic workloads, that 3.2 percentage points is meaningful. It is the difference between a clean loop and a retry round-trip, which compounds in latency and cost.
Long-form drafting
On N1 (3,000-word article from outline), GPT-5.5 wins outright with the strongest faithfulness-to-outline plus voice consistency. The competing Anthropic and Google models are fluent but tend to drift away from the bullet structure partway through.
Structured output
N8 winner. Schema-strict JSON mode produces fully-valid output 98.1% of the time across our N8 prompts. Opus 4.7 is at 95.4% and Gemini at 96.7%.
Weaknesses and Failure Modes
Multi-file refactor loss to Opus
GPT-5.5 produces high-quality single-file edits but loses cross-file consistency more often than Opus 4.7 on P1. The specific failure mode: it identifies the changes to make but applies them inconsistently across the affected files, leaving tests broken in 3-4 places.
Domain QA fabrication
N9 fabrication rate is 5.1%, highest of the frontier four. The model is more likely to confidently invent a citation under retrieval pressure than Opus 4.7 (3.2%) or Gemini (4.4%). This is the single biggest reason to look elsewhere for regulated-domain QA.
Cost
Standard tier $30 output is the highest among non-Pro frontier. Pro tier $180 is 7x the next-most-expensive frontier output rate. For high-volume workloads, GPT-5.5 is the costly choice unless you have a specific reason to need it.
When to Use GPT-5.5
- General-purpose default. If you do not know what your workload mix will look like, GPT-5.5 standard is the lowest-regret pick.
- Tool-using agents. Especially in production, where a 3.2 pp tool-call success advantage compounds.
- Long-form drafting and JSON-strict output. The category winners.
- GPT-5.5 Pro: hardest reasoning only. Deploy as a per-call escalation tier, not a default.
When NOT to Use GPT-5.5
- Multi-file refactor / migration scripts. Use Claude Opus 4.7.
- Translation, especially low-resource languages. Use Gemini 3.1 Pro.
- Strict-fidelity domain QA where one hallucinated citation is unacceptable. Use Opus 4.7.
- Cost-sensitive bulk extraction. DeepSeek V4 Pro at one-tenth the cost is roughly substitutable.
Comparison to Direct Rivals
vs Claude Opus 4.7
| Dimension | GPT-5.5 | Opus 4.7 |
|---|---|---|
| Output price ($/1M) | $30 | $25 |
| Context window | 1M | 500K |
| AAII Index | 59 | 58.1 |
| Tool-call success | 97.4% | 94.2% |
| Hallucination rate (N9) | 5.1% | 3.2% |
vs Gemini 3.1 Pro
| Dimension | GPT-5.5 | Gemini 3.1 Pro |
|---|---|---|
| Input price ($/1M) | $5.00 | $3.50 |
| Context window | 1M | 2M |
| GPQA Diamond | 89.7 | 94.3 |
| Long-form drafting (N1) | 91 | 89 |
| Translation (N5) | 84 | 92 |
Procurement Notes
Enterprise readiness
The most mature procurement story in the market. Available via OpenAI direct, Azure OpenAI, Microsoft 365 Copilot. Full SOC 2 Type II, ISO 27001, HIPAA, PCI DSS available. Custom data retention and zero-day-retention configurations standard on Enterprise plans.
Lock-in score
4.0 / 5: highest in the closed-frontier tier. Specific switching costs: OpenAI's function-calling schema shape that doesn't map cleanly to Anthropic's tool schema, persistent quirks in JSON output handling that target code expects, and Azure-specific IAM coupling on the gateway path. Swfte Connect specifically exists to abstract this away.
Contract use
OpenAI's direct enterprise tier offers volume discounts starting at ~$50K/month and significantly improves at $500K+. Azure adds Azure-committed-use use. The Pro tier is rarely negotiable on price; OpenAI treats it as a premium per-call tier rather than a contract line item.