Long documents
Prompts that approach the documented context limit
GPT-5.5
GPT-5.5 has the larger documented context window.
Confidence: documented
Command A+ vs GPT-5.5
Updated October 2, 2026. Rank says GPT-5.5 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
GPT-5.5 has the higher public point estimate, 69.4 versus 36.58. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 3
- Command A+ only
- 2
- GPT-5.5 only
- 37
- Like-for-like categories
- 0 / 8
Estimated: Command A+ · Supported: GPT-5.5. Conditional ranges do not establish rank confidence.How the comparison works
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.
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-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Command A+ is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Command A+ is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedCache-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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
23.3Command A+62.7GPT-5.5
Directional only · BenchAlign v5.8
GPT-5.5 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 Command A+ only
3 Shared
37 GPT-5.5 only
5 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
MMMU-ProMultimodal
Normalized gap 18.2Command A+:63%GPT-5.5:81.2%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Agentic
Directional only- Command A+
- 15.9
- Estimated · #102/119
- GPT-5.5
- 59.9
- Supported · #22/119
- Basis
- BenchAlign v5.8 lane · 2 vs 14 public rows
- Reading
- Directional only
Coding
Directional only- Command A+
- 23.3
- Estimated · #112/144
- GPT-5.5
- 62.7
- Supported · #13/144
- Basis
- BenchAlign v5.8 lane · 0 vs 10 public rows
- Reading
- Directional only
Multimodal
Directional only- Command A+
- 16.1
- #48/49
- GPT-5.5
- 71.4
- #19/49
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Directional only
Knowledge
Directional only- Command A+
- 27.7
- Estimated · #145/171
- GPT-5.5
- 69.4
- Supported · #15/171
- Basis
- BenchAlign v5.8 lane · 0 vs 6 public rows
- Reading
- Directional only
Instruction following
Directional only- Command A+
- 89.2
- #20/125
- GPT-5.5
- 91.9
- #7/125
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Command A+
- 58.5
- Unranked · 2 rankable rows
- GPT-5.5
- 66.2
- #17/27
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Command A+
- Not ranked
- GPT-5.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Command A+
- Not ranked
- GPT-5.5
- 69.4
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Command A+ | GPT-5.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 15.9Estimated · #102/119 | 59.9Supported · #22/119 | Directional onlyBenchAlign v5.8 lane · 2 vs 14 public rows | Directional only |
| Coding | 23.3Estimated · #112/144 | 62.7Supported · #13/144 | Directional onlyBenchAlign v5.8 lane · 0 vs 10 public rows | Directional only |
| Multimodal | 16.1#48/49 | 71.4#19/49 | Directional onlyProvisional lane · 2 vs 2 weighted rows | Directional only |
| Knowledge | 27.7Estimated · #145/171 | 69.4Supported · #15/171 | Directional onlyBenchAlign v5.8 lane · 0 vs 6 public rows | Directional only |
| Instruction following | 89.2#20/125 | 91.9#7/125 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 58.5Unranked · 2 rankable rows | 66.2#17/27 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 69.4Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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-5.5
- $0.02
- 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-5.5
- $0.34
- 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-5.5
- $0.5
- 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.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationCommand A+GPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
Command A+
128K
GPT-5.5
API model ID
Command A+
Not sourced
GPT-5.5
gpt-5.5
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-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Command A+
Not sourced
GPT-5.5
Not sourced
Documented outputs
Command A+
Not sourced
GPT-5.5
Not sourced
Provider availability
Command A+
Not sourced
GPT-5.5
Not sourced
Reasoning profile
Command A+
Reasoning
GPT-5.5
Reasoning
Weight access
Command A+
Open Weight
GPT-5.5
Proprietary
License
Command A+
Open Weight
GPT-5.5
Proprietary
Release date
Command A+
2026-05-20
GPT-5.5
2026-04-23
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-5.5 has the higher public point estimate, 69.4 versus 36.58. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
- Workload cost
- Repository review: $0.155 vs $0.34. Cache-heavy agent loop: $0.65 vs $0.5.
- Context tradeoff
- GPT-5.5 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Command A+ or GPT-5.5?
GPT-5.5 has the higher public point estimate, 69.4 versus 36.58. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Command A+ or GPT-5.5?
GPT-5.5 scores higher for coding on the public lane, 62.7 to 23.3. Command A+ is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Command A+ or GPT-5.5?
GPT-5.5 scores higher for agentic tasks on the public lane, 59.9 to 15.9. Command A+ is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Command A+ or GPT-5.5?
For the stated presets, chat costs $0.0075 on Command A+ and $0.02 on GPT-5.5; repository review costs $0.155 and $0.34; the cache-heavy agent loop costs $0.65 and $0.5. 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-5.5?
GPT-5.5 has the larger documented context window: 1M, compared with 128K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence42 rows
Agentic
τ²-bench results
Command A+85%SourceGPT-5.598%SourceGPT-5.5 leads this result
Terminal-Bench 2.1 (Vals)
Command A+17.6%SourceGPT-5.576.4%SourceGPT-5.5 leads this result
Terminal-Bench 2.0
Command A+—GPT-5.582%SourceNot directly comparable
CyberGym
Command A+—GPT-5.581.8%SourceNot directly comparable
BrowseComp
Command A+—GPT-5.584.4%SourceNot directly comparable
OSWorld-Verified
Command A+—GPT-5.578.7%SourceNot directly comparable
MCP Atlas
Command A+—GPT-5.575.3%SourceNot directly comparable
Toolathlon
Command A+—GPT-5.555.6%SourceNot directly comparable
Gert Labs
Command A+—GPT-5.572.93%SourceNot directly comparable
ResearchClawBench
Command A+—GPT-5.517.0%SourceNot directly comparable
OSWorld 2.0
Command A+—GPT-5.513.0%SourceNot directly comparable
JobBench
Command A+—GPT-5.542.7%SourceNot directly comparable
ExploitGym
Command A+—GPT-5.513.4%SourceNot directly comparable
ApprenticeBench
Command A+—GPT-5.520%SourceNot directly comparable
Coding
SWE-bench Pro
Command A+—GPT-5.558.6%SourceNot directly comparable
Terminal-Bench 2.0
Command A+—GPT-5.582.0%SourceNot directly comparable
Vibe Code Bench
Command A+—GPT-5.569.85%SourceNot directly comparable
React Native Evals
Command A+—GPT-5.584.7%SourceNot directly comparable
cursorBench31
Command A+—GPT-5.559.2%SourceNot directly comparable
CursorBench 3.2
Command A+—GPT-5.558.4%SourceNot directly comparable
FrontierCode 1.1 Main
Command A+—GPT-5.543.0%SourceNot directly comparable
LiveCodeBench (Vals)
Command A+—GPT-5.585.3%SourceNot directly comparable
SWE-bench (Vals)
Command A+—GPT-5.582.6%SourceNot directly comparable
PostTrainBench v1.1
Command A+—GPT-5.527.2%SourceNot directly comparable
Reasoning
Multimodal
MMMU
Command A+75.1%SourceGPT-5.5—Not directly comparable
MMMU-Pro
Command A+63%SourceGPT-5.581.2%SourceGPT-5.5 leads this result
CharXiv
Command A+52.7%SourceGPT-5.5—Not directly comparable
MMMU-Pro w/ Python
Command A+—GPT-5.583.2%SourceNot directly comparable
OfficeQA Pro
Command A+—GPT-5.554.1%SourceNot directly comparable
Knowledge
GPQA
Command A+—GPT-5.593.6%SourceNot directly comparable
GPQA-D
Command A+—GPT-5.593.6%SourceNot directly comparable
HLE
Command A+—GPT-5.552.2%SourceNot directly comparable
HLE w/o tools
Command A+—GPT-5.541.4%SourceNot directly comparable
GPQA Diamond (Vals)
Command A+—GPT-5.593.2%SourceNot directly comparable
MMLU-Pro (Vals)
Command A+—GPT-5.588.1%SourceNot directly comparable
Math
42 public results · 3 shared
Watch Command A+ vs GPT-5.5
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Last updated October 2, 2026