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Model comparison

GPT-5.5 vs Step 3.7 Flash

Data verified

Head-to-head evidence from 25 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

GPT-5.5
OpenAI
76.14/100
Margin
25.4pts
← winning
50.76/100
2 category wins0 category wins

Verified leaderboard positions: GPT-5.5 #3; Step 3.7 Flash unranked

BenchAlign evidence: GPT-5.5 supported; Step 3.7 Flash estimated. Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GPT-5.5 and Step 3.7 Flash share 25 comparable benchmark results. 2 of 8 categories are comparable. 32 results are unique to GPT-5.5; 5 to Step 3.7 Flash.

Updated July 16, 2026
Shared results
25
GPT-5.5 only
32
Step 3.7 Flash only
5
Comparable categories
2 / 8

Pick GPT-5.5 if you want the stronger benchmark profile. Step 3.7 Flash only becomes the better choice if you want the cheaper token bill.

Confidence note. This is a partial-evidence comparison with 25 shared benchmark results across 6 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

GPT-5.5 is clearly ahead on the provisional aggregate, 78 to 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

GPT-5.5's sharpest advantage is in agentic, where it averages 81.6 against 66.4. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 82% to 59.5%.

GPT-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $0.20 input / $1.15 output per 1M tokens for Step 3.7 Flash. That is roughly 26.1x on output cost alone. GPT-5.5 gives you the larger context window at 1M, compared with 256K for Step 3.7 Flash.

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 scores and score margins for GPT-5.5 and Step 3.7 Flash
CategoryGPT-5.5ΔStep 3.7 Flash
AgenticGPT-5.581.6Margin← 15.2Step 3.7 Flash66.4
CodingGPT-5.558.6Margin← 2.3Step 3.7 Flash56.3
ReasoningGPT-5.585.0MarginNo overlapStep 3.7 FlashNot measured
KnowledgeGPT-5.557.8MarginNo overlapStep 3.7 FlashNot measured
MathGPT-5.547.6MarginNo overlapStep 3.7 FlashNot measured
MultimodalGPT-5.570.4MarginNo overlapStep 3.7 FlashNot measured

Decisive benchmark drivers

The largest measured benchmark gaps in this matchup, with exact reported values.

More
A · GPT-5.5B · Step 3.7 Flash

  1. Terminal-Bench 2.0

    Agentic
    Source ↗
    A 82%B 59.5%
    Winner: GPT-5.5Δ 22.5
    Terminal-Bench 2.0: GPT-5.5 scored 82%; Step 3.7 Flash scored 59.5%. GPT-5.5 wins this benchmark.

  2. BrowseComp

    Agentic
    Source ↗
    A 84.4%B 75.8%
    Winner: GPT-5.5Δ 8.6
    BrowseComp: GPT-5.5 scored 84.4%; Step 3.7 Flash scored 75.8%. GPT-5.5 wins this benchmark.

  3. SWE-bench Pro

    Coding
    Source ↗
    A 58.6%B 56.3%
    Winner: GPT-5.5Δ 2.3
    SWE-bench Pro: GPT-5.5 scored 58.6%; Step 3.7 Flash scored 56.3%. GPT-5.5 wins this benchmark.

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricGPT-5.5Step 3.7 FlashComparison
Input / output priceUSD per 1M tokensGPT-5.5$5 input / $30 outputStep 3.7 Flash$0.2 input / $1.15 outputStep 3.7 Flash has the lower combined listed price.
Generation speedtokens per secondGPT-5.5Not availableStep 3.7 FlashNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGPT-5.5Not availableStep 3.7 FlashNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGPT-5.51MStep 3.7 Flash256KGPT-5.5 lists the larger context window.

Benchmark Deep Dive

AgenticGPT-5.5 wins
BenchmarkGPT-5.5Step 3.7 FlashResult
Terminal-Bench 2.0Source 82%59.5%GPT-5.5 leads
CyberGymSource 81.8%—Not comparable
BrowseCompSource 84.4%75.8%GPT-5.5 leads
OSWorld-VerifiedSource 78.7%—Not comparable
MCP AtlasSource 75.3%—Not comparable
ToolathlonSource 55.6%49.5%GPT-5.5 leads
τ²-bench resultsSource 93.9%98.5%Step 3.7 Flash leads
AA Agentic IndexSource 44.9%21.5%GPT-5.5 leads
APEX-Agents-AASource 37.7%14.8%GPT-5.5 leads
GDPval-AASource 49.7%25.9%GPT-5.5 leads
GDPval-AASource 14931017GPT-5.5 leads
Gert LabsSource 72.93%51.57%GPT-5.5 leads
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 4.2%—Not comparable
AA ITBenchSource 45.8%—Not comparable
AA Tau3 BankingSource 31.3%—Not comparable
DeepSearchQASource —92.8%Not comparable
Claw-EvalSource —67.1%Not comparable
HLE w/ toolsSource —47.2%Not comparable
CodingGPT-5.5 wins
BenchmarkGPT-5.5Step 3.7 FlashResult
SWE-bench ProSource 58.6%56.3%GPT-5.5 leads
Terminal-Bench 2.0Source 82.0%59.5%GPT-5.5 leads
Vibe Code BenchSource 69.85%—Not comparable
React Native EvalsSource 84.7%—Not comparable
cursorBench31Source 59.2%—Not comparable
cursorBench32Source 58.4%—Not comparable
AA Coding IndexSource 74.9%39.6%GPT-5.5 leads
Terminal-Bench HardSource 60.6%35.6%GPT-5.5 leads
AA-SciCodeSource 56.1%40.0%GPT-5.5 leads
FrontierCodeSource 43.0%—Not comparable
AA Terminal-Bench 2.1Source 84.3%—Not comparable
Reasoning
BenchmarkGPT-5.5Step 3.7 FlashResult
MRCR v2 64K-128KSource 83.1%—Not comparable
MRCR v2 128K-256KSource 87.5%—Not comparable
ARC-AGI-2Source 85%—Not comparable
AA-LCRSource 74.3%63.7%GPT-5.5 leads
CritPtSource 27.1%2.3%GPT-5.5 leads
Knowledge
BenchmarkGPT-5.5Step 3.7 FlashResult
GPQASource 93.6%—Not comparable
GPQA-DSource 93.6%—Not comparable
HLESource 52.2%—Not comparable
HLE w/o toolsSource 41.4%—Not comparable
Artificial Analysis Intelligence IndexSource 54.8%30.3%GPT-5.5 leads
AA-GPQA DiamondSource 93.5%80.9%GPT-5.5 leads
AA-HLESource 44.3%19.9%GPT-5.5 leads
AA-Omniscience IndexSource 20.1%-37.5%GPT-5.5 leads
AA-Omniscience AccuracySource 56.9%25.4%GPT-5.5 leads
AA-Omniscience Hallucination RateSource 85.5%84.4%Step 3.7 Flash leads
Math
BenchmarkGPT-5.5Step 3.7 FlashResult
FrontierMath (legacy)Source 51.7%—Not comparable
FrontierMath v2 (Tiers 1-3)Source 51.700%—Not comparable
FrontierMath v2 (Tier 4)Source 35.400%—Not comparable
Multimodal
BenchmarkGPT-5.5Step 3.7 FlashResult
MMMU-ProSource 81.2%—Not comparable
MMMU-Pro w/ PythonSource 83.2%—Not comparable
OfficeQA ProSource 54.1%—Not comparable
AA-MMMU-ProSource 79.9%75.3%GPT-5.5 leads
Design Arena WebsiteSource 12871218GPT-5.5 leads
SimpleVQASource —79.2%Not comparable
V*Source —95.3%Not comparable
Inst. Following
BenchmarkGPT-5.5Step 3.7 FlashResult
AA-IFBenchSource 75.9%67.3%GPT-5.5 leads
Frequently Asked Questions (3)

Which is better, GPT-5.5 or Step 3.7 Flash?

GPT-5.5 is ahead on BenchLM's provisional leaderboard, 78 to 57. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 82% and 59.5%.

Which is better for coding, GPT-5.5 or Step 3.7 Flash?

GPT-5.5 has the edge for coding in this comparison, averaging 58.6 versus 56.3. Inside this category, AA Coding Index is the benchmark that creates the most daylight between them.

Which is better for agentic tasks, GPT-5.5 or Step 3.7 Flash?

GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.6 versus 66.4. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.

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