Model comparison
GPT-4 Turbo vs GPT-5.5
Head-to-head evidence from 4 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: GPT-4 Turbo #188 (Supported); GPT-5.5 #9 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. GPT-4 Turbo and GPT-5.5 share 4 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to GPT-4 Turbo; 53 to GPT-5.5.
Updated July 22, 2026- Shared results
- 4
- GPT-4 Turbo only
- 0
- GPT-5.5 only
- 53
- Comparable categories
- 0 / 8
Benchmark data for GPT-4 Turbo and GPT-5.5 is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 4 shared benchmark results across 2 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
GPT-5.5 has the larger context window at 1M, compared with 128K for GPT-4 Turbo.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | GPT-4 Turbo | Δ | GPT-5.5 |
|---|---|---|---|
| Agentic | GPT-4 TurboNot measured | MarginNo overlap | GPT-5.581.6 |
| Coding | GPT-4 TurboNot measured | MarginNo overlap | GPT-5.558.6 |
| Reasoning | GPT-4 TurboNot measured | MarginNo overlap | GPT-5.585.0 |
| Knowledge | GPT-4 TurboNot measured | MarginNo overlap | GPT-5.557.8 |
| Math | GPT-4 TurboNot measured | MarginNo overlap | GPT-5.547.6 |
| Multimodal | GPT-4 TurboNot measured | MarginNo overlap | GPT-5.570.4 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | GPT-4 Turbo | GPT-5.5 | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | GPT-4 Turbo$10 input / $30 output | GPT-5.5$5 input / $30 output | GPT-5.5 has the lower combined listed price. |
| Generation speedtokens per second | GPT-4 Turbo30 tok/s | GPT-5.5Not available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | GPT-4 Turbo2.84 s | GPT-5.5Not available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | GPT-4 Turbo128K | GPT-5.51M | GPT-5.5 lists the larger context window. |
Benchmark Deep Dive
Agentic24 benchmarks
| Benchmark | GPT-4 Turbo | GPT-5.5 | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | — | 82% | Not comparable |
| CyberGymSource | — | 81.8% | Not comparable |
| BrowseCompSource | — | 84.4% | Not comparable |
| OSWorld-VerifiedSource | — | 78.7% | Not comparable |
| MCP AtlasSource | — | 75.3% | Not comparable |
| ToolathlonSource | — | 55.6% | Not comparable |
| τ²-bench resultsSource | — | 93.9% | Not comparable |
| AA Agentic IndexSource | — | 44.9% | Not comparable |
| APEX-Agents-AASource | — | 37.7% | Not comparable |
| GDPval-AASource | — | 49.5% | Not comparable |
| GDPval-AASource | — | 1490 | Not comparable |
| Gert LabsSource | — | 72.93% | Not comparable |
| ResearchClawBenchSource | — | 17.0% | Not comparable |
| OSWorld 2.0Source | — | 13.0% | Not comparable |
| JobBenchSource | — | 42.7% | Not comparable |
| ExploitGymSource | — | 13.4% | Not comparable |
| AA BriefcaseSource | — | 1154 | Not comparable |
| AA AutomationBenchSource | — | 42.1% | Not comparable |
| AA EnterpriseOps-GymSource | — | 46.6% | Not comparable |
| AA Harvey LABSource | — | 86.3% | Not comparable |
| AA ITBenchSource | — | 45.8% | Not comparable |
| AA Tau3 BankingSource | — | 31.3% | Not comparable |
| terminalBenchHardSource | — | 60.6% | Not comparable |
| aaTerminalBench21Source | — | 84.3% | Not comparable |
Coding9 benchmarks
| Benchmark | GPT-4 Turbo | GPT-5.5 | Result |
|---|---|---|---|
| AA Coding IndexSource | 21.5% | 74.9% | GPT-5.5 leads |
| AA-SciCodeSource | 31.9% | 56.1% | GPT-5.5 leads |
| SWE-bench ProSource | — | 58.6% | Not comparable |
| Terminal-Bench 2.0Source | — | 82.0% | Not comparable |
| Vibe Code BenchSource | — | 69.85% | Not comparable |
| React Native EvalsSource | — | 84.7% | Not comparable |
| cursorBench31Source | — | 59.2% | Not comparable |
| cursorBench32Source | — | 58.4% | Not comparable |
| FrontierCode 1.1 MainSource | — | 43.0% | Not comparable |
Reasoning5 benchmarks
Knowledge10 benchmarks
| Benchmark | GPT-4 Turbo | GPT-5.5 | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 7.9% | 54.8% | GPT-5.5 leads |
| AA-HLESource | 3.3% | 44.3% | GPT-5.5 leads |
| GPQASource | — | 93.6% | Not comparable |
| GPQA-DSource | — | 93.6% | Not comparable |
| HLESource | — | 52.2% | Not comparable |
| HLE w/o toolsSource | — | 41.4% | Not comparable |
| AA-GPQA DiamondSource | — | 93.5% | Not comparable |
| AA-Omniscience IndexSource | — | 20.1% | Not comparable |
| AA-Omniscience AccuracySource | — | 56.9% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 85.5% | Not comparable |
Math3 benchmarks
Multimodal5 benchmarks
Inst. Following1 benchmarks
| Benchmark | GPT-4 Turbo | GPT-5.5 | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 75.9% | Not comparable |
Frequently Asked Questions (3)
Can I compare GPT-4 Turbo and GPT-5.5 on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for GPT-4 Turbo and GPT-5.5 today?
GPT-4 Turbo: $10.00 input / $30.00 output per 1M tokens GPT-5.5: $5.00 input / $30.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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