Multimodal
Directional only- Command A+
- 59.3
- GPT-5.4
- 73.2
- Weighted basis
- 2 vs 3 rows
- Reading
- Directional only
Command A+ vs GPT-5.4
Updated August 18, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.4 has the higher public score, 73.36 versus 47.56, and the 90% score intervals do not overlap.
3 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
GPT-5.4 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Command A+
Command A+ has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Command A+
Command A+ 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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Command A+ does not fit this workload in one request. Command A+ has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 Command A+ only
3 Shared
35 GPT-5.4 only
- Shared results
- 3
- Command A+ only
- 1
- GPT-5.4 only
- 35
- 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.
Agentic
Not comparable- Command A+
- Not measured
- GPT-5.4
- 77.2
- Weighted basis
- 0 vs 3 rows
- Reading
- Not comparable
Coding
Not comparable- Command A+
- Not measured
- GPT-5.4
- 57.7
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Reasoning
Not comparable- Command A+
- Not measured
- GPT-5.4
- 74.0
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
Knowledge
Not comparable- Command A+
- Not measured
- GPT-5.4
- 57.6
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Math
Not comparable- Command A+
- Not measured
- GPT-5.4
- 42.5
- Weighted basis
- 0 vs 2 rows
- Reading
- Not comparable
Multilingual
Not comparable- Command A+
- Not measured
- GPT-5.4
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Command A+
- Not measured
- GPT-5.4
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Command A+ | GPT-5.4 | Weighted basis | Reading |
|---|---|---|---|---|
| Multimodal | 59.3 | 73.2 | Directional only2 vs 3 rows | Directional only |
| Agentic | Not measured | 77.2 | Not comparable0 vs 3 rows | Not comparable |
| Coding | Not measured | 57.7 | Not comparable0 vs 1 rows | Not comparable |
| Reasoning | Not measured | 74.0 | Not comparable0 vs 1 rows | Not comparable |
| Knowledge | Not measured | 57.6 | Not comparable0 vs 2 rows | Not comparable |
| Math | Not measured | 42.5 | Not comparable0 vs 2 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 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.
CharXiv
Multimodal
MMMU-Pro
Multimodal
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
- Command A+
- $0.0075
- Fits in one request
- GPT-5.4
- $0.01
- Fits in one request
Command A+ has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Command A+
- $0.155
- Fits in one request
- GPT-5.4
- $0.17
- Fits in one request
Command A+ has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Command A+
- $0.65
- Does not fit in one request
- Cached input priced at the published list-input rate
- GPT-5.4
- $0.25
- Fits in one request
Command A+ does not fit this workload in one request. Command A+ 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.
SpecificationCommand A+GPT-5.4
Context window
Maximum documented context; output-token limits may be lower.
Command A+
128K
GPT-5.4
1.05M
OpenAI pricingAPI model ID
Command A+
Not sourced
GPT-5.4
gpt-5.4
OpenAI pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Command A+
Not published
GPT-5.4
$0.25 per 1M cached input tokens
OpenAI pricingDocumented inputs
Command A+
Not sourced
GPT-5.4
Not sourced
Documented outputs
Command A+
Not sourced
GPT-5.4
Not sourced
Provider availability
Command A+
Not sourced
GPT-5.4
Not sourced
Reasoning profile
Command A+
Reasoning
GPT-5.4
Reasoning
Weight access
Command A+
Open Weight
GPT-5.4
Proprietary
License
Command A+
Open Weight
GPT-5.4
Proprietary
Release date
Command A+
2026-05-20
GPT-5.4
2026-03-05
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 has the higher public score, 73.36 versus 47.56, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.155 vs $0.17. Cache-heavy agent loop: $0.65 vs $0.25.
- Context tradeoff
- GPT-5.4 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 evidence39 rows
Agentic
τ²-bench results
Command A+85%SourceGPT-5.498.9%SourceGPT-5.4 leads this result
Terminal-Bench 2.0
Command A+—GPT-5.475.1%SourceNot directly comparable
CyberGym
Command A+—GPT-5.479.0%SourceNot directly comparable
BrowseComp
Command A+—GPT-5.482.7%SourceNot directly comparable
OSWorld-Verified
Command A+—GPT-5.475%SourceNot directly comparable
MCP Atlas
Command A+—GPT-5.470.6%SourceNot directly comparable
Toolathlon
Command A+—GPT-5.454.6%SourceNot directly comparable
Claw-Eval
Command A+—GPT-5.460.3%SourceNot directly comparable
DeepSearchQA
Command A+—GPT-5.473.6%SourceNot directly comparable
Gert Labs
Command A+—GPT-5.464.89%SourceNot directly comparable
ResearchClawBench
Command A+—GPT-5.415.3%SourceNot directly comparable
JobBench
Command A+—GPT-5.438.9%SourceNot directly comparable
ExploitGym
Command A+—GPT-5.46.0%SourceNot directly comparable
Coding
LiveCodeBench Pro
Command A+—GPT-5.487.5%SourceNot directly comparable
SWE-bench Pro
Command A+—GPT-5.457.7%SourceNot directly comparable
React Native Evals
Command A+—GPT-5.485.3%SourceNot directly comparable
Vibe Code Bench
Command A+—GPT-5.467.42%SourceNot directly comparable
EEBench
Command A+—GPT-5.438.5%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Command A+—GPT-5.492.8%SourceNot directly comparable
HLE
Command A+—GPT-5.452.1%SourceNot directly comparable
HLE w/o tools
Command A+—GPT-5.439.8%SourceNot directly comparable
GPQA-D
Command A+—GPT-5.492.8%SourceNot directly comparable
HealthBench Hard
Command A+—GPT-5.440.1%SourceNot directly comparable
MedXpertQA (Text)
Command A+—GPT-5.459.6%SourceNot directly comparable
HealthBench Professional
Command A+—GPT-5.448.1%SourceNot directly comparable
Math
Multimodal
MMMU
Command A+75.1%SourceGPT-5.4—Not directly comparable
MMMU-Pro
Command A+63%SourceGPT-5.481.2%SourceGPT-5.4 leads this result
CharXiv
Command A+52.7%SourceGPT-5.482.8%SourceGPT-5.4 leads this result
OfficeQA Pro
Command A+—GPT-5.453.2%SourceNot directly comparable
MMMU-Pro w/ Python
Command A+—GPT-5.482.1%SourceNot directly comparable
ERQA
Command A+—GPT-5.465.4%SourceNot directly comparable
SimpleVQA
Command A+—GPT-5.461.1%SourceNot directly comparable
ScreenSpot Pro
Command A+—GPT-5.485.4%SourceNot directly comparable
ZeroBench
Command A+—GPT-5.441.0%SourceNot directly comparable
MedXpertQA (MM)
Command A+—GPT-5.477.1%SourceNot directly comparable
Frequently asked questions
Which is better, Command A+ or GPT-5.4?
GPT-5.4 has the higher public score, 73.36 versus 47.56, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Command A+ or GPT-5.4?
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, Command A+ or GPT-5.4?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, Command A+ or GPT-5.4?
For the stated presets, chat costs $0.0075 on Command A+ and $0.01 on GPT-5.4; repository review costs $0.155 and $0.17; the cache-heavy agent loop costs $0.65 and $0.25. Command A+ does not fit this workload in one request. Command A+ has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Command A+ or GPT-5.4?
GPT-5.4 has the larger documented context window: 1.05M, compared with 128K.
Related comparisons
Last updated August 18, 2026
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