Agentic
Directional only- GPT-5.4 Pro
- 89.3
- Step 3.7 Flash
- 66.4
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
GPT-5.4 Pro vs Step 3.7 Flash
Updated August 30, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.4 Pro has the higher public score estimate, 61.53 versus 51.05, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Long documents
Prompts that approach the documented context limit
GPT-5.4 Pro
GPT-5.4 Pro has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Step 3.7 Flash
Step 3.7 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Step 3.7 Flash
Step 3.7 Flash has the lower estimated token cost for this stated workload. GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate. Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Step 3.7 Flash
Step 3.7 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
10 GPT-5.4 Pro only
1 Shared
10 Step 3.7 Flash only
- Shared results
- 1
- GPT-5.4 Pro only
- 10
- Step 3.7 Flash only
- 10
- Like-for-like categories
- 0 / 8
1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Not comparable- GPT-5.4 Pro
- Not measured
- Step 3.7 Flash
- 56.3
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.4 Pro
- 83.3
- Step 3.7 Flash
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.4 Pro
- 58.7
- Step 3.7 Flash
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.4 Pro
- 46.9
- Step 3.7 Flash
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.4 Pro
- Not measured
- Step 3.7 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.4 Pro
- 94.0
- Step 3.7 Flash
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.4 Pro
- Not measured
- Step 3.7 Flash
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.4 Pro | Step 3.7 Flash | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 89.3 | 66.4 | Directional only1 vs 2 rows | Directional only |
| Coding | Not measured | 56.3 | Not comparable0 vs 1 rows | Not comparable |
| Reasoning | 83.3 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Knowledge | 58.7 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 46.9 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 94.0 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
BrowseComp
Agentic
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- GPT-5.4 Pro
- $0.12
- Fits in one request
- Step 3.7 Flash
- $0.00077
- Fits in one request
Step 3.7 Flash has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.4 Pro
- $2.04
- Fits in one request
- Step 3.7 Flash
- $0.01345
- Fits in one request
Step 3.7 Flash has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.4 Pro
- $8.40
- Fits in one request
- Cached input priced at the published list-input rate
- Step 3.7 Flash
- $0.0555
- Fits in one request
- Cached input priced at the published list-input rate
Step 3.7 Flash has the lower modeled cost
GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate. Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.4 ProStep 3.7 Flash
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.4 Pro
Step 3.7 Flash
256K
API model ID
GPT-5.4 Pro
gpt-5.4-pro
OpenAI GPT-5.4 Pro model documentationStep 3.7 Flash
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.4 Pro
Not published
OpenAI pricingStep 3.7 Flash
Not published
Documented inputs
GPT-5.4 Pro
text, image
OpenAI model catalogStep 3.7 Flash
Not sourced
Documented outputs
GPT-5.4 Pro
Step 3.7 Flash
Not sourced
Provider availability
GPT-5.4 Pro
Generally Available · OpenAI Responses API
OpenAI model catalogStep 3.7 Flash
Not sourced
Reasoning profile
GPT-5.4 Pro
Reasoning
Step 3.7 Flash
Reasoning
Weight access
GPT-5.4 Pro
Proprietary
Step 3.7 Flash
Open Weight
License
GPT-5.4 Pro
Proprietary
Step 3.7 Flash
Open Weight
Release date
GPT-5.4 Pro
2026-03-05
Step 3.7 Flash
2026-05-29
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- GPT-5.4 Pro has the higher public score estimate, 61.53 versus 51.05, but the 90% score intervals overlap.
- Workload cost
- Repository review: $2.04 vs $0.01345. Cache-heavy agent loop: $8.40 vs $0.0555.
- Context tradeoff
- GPT-5.4 Pro has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence21 rows
Agentic
BrowseComp
GPT-5.4 Pro89.3%SourceStep 3.7 Flash75.8%SourceGPT-5.4 Pro leads this result
Terminal-Bench 2.0
GPT-5.4 Pro—Step 3.7 Flash59.5%SourceNot directly comparable
DeepSearchQA
GPT-5.4 Pro—Step 3.7 Flash92.8%SourceNot directly comparable
Toolathlon
GPT-5.4 Pro—Step 3.7 Flash49.5%SourceNot directly comparable
Claw-Eval
GPT-5.4 Pro—Step 3.7 Flash67.1%SourceNot directly comparable
HLE w/ tools
GPT-5.4 Pro—Step 3.7 Flash47.2%SourceNot directly comparable
Gert Labs
GPT-5.4 Pro—Step 3.7 Flash51.57%SourceNot directly comparable
Coding
Reasoning
ARC-AGI-2
GPT-5.4 Pro83.3%SourceStep 3.7 Flash—Not directly comparable
Knowledge
Math
IPhO 2025 (Theory)
GPT-5.4 Pro93.5%SourceStep 3.7 Flash—Not directly comparable
FrontierMath (legacy)
GPT-5.4 Pro50%SourceStep 3.7 Flash—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.4 Pro50.000%SourceStep 3.7 Flash—Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.4 Pro37.500%SourceStep 3.7 Flash—Not directly comparable
Multimodal
Frequently asked questions
Which is better, GPT-5.4 Pro or Step 3.7 Flash?
GPT-5.4 Pro has the higher public score estimate, 61.53 versus 51.05, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-5.4 Pro or Step 3.7 Flash?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-5.4 Pro or Step 3.7 Flash?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-5.4 Pro or Step 3.7 Flash?
For the stated presets, chat costs $0.12 on GPT-5.4 Pro and $0.00077 on Step 3.7 Flash; repository review costs $2.04 and $0.01345; the cache-heavy agent loop costs $8.40 and $0.0555. GPT-5.4 Pro has no published cached-input rate, so cached tokens use its listed input rate. Step 3.7 Flash has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.4 Pro or Step 3.7 Flash?
GPT-5.4 Pro has the larger documented context window: 1.05M, compared with 256K.
Related comparisons
Last updated August 30, 2026
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