Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Sol
GPT-5.6 Sol leads on the public coding lane, 71.6 to 64.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
GPT-5.5 vs GPT-5.6 Sol
Updated September 24, 2026. Rank says GPT-5.6 Sol 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
Share or export
Decision reading
GPT-5.6 Sol has the higher public score, 78.49 versus 69.13, and the 90% score intervals do not overlap. 22 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 22
- GPT-5.5 only
- 17
- GPT-5.6 Sol only
- 15
- Like-for-like categories
- 4 / 8
Supported: GPT-5.5 and GPT-5.6 SolHow 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.
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Sol
GPT-5.6 Sol leads on the public agentic lane, 69.6 to 60.1, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limitedLong documents
Prompts that approach the documented context limit
GPT-5.6 Sol
GPT-5.6 Sol has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.6 Sol
GPT-5.6 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.6 Sol
GPT-5.6 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
GPT-5.6 Sol
GPT-5.6 Sol has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
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.
64.1GPT-5.571.6GPT-5.6 Sol
Like-for-like · BenchAlign v5.7
GPT-5.6 Sol leads the like-for-like coding row, although the 90% intervals overlap.
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.
17 GPT-5.5 only
22 Shared
15 GPT-5.6 Sol only
2 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.
OSWorld 2.0Agentic
Normalized gap 49.6GPT-5.5:13.0%GPT-5.6 Sol:62.6%FrontierMath v2 (Tier 4)Math
Normalized gap 47.6GPT-5.5:35.400%GPT-5.6 Sol:83.000%FrontierMath v2 (Tiers 1-3)Math
Normalized gap 37.3GPT-5.5:51.700%GPT-5.6 Sol:89.000%cursorBench32Coding
Normalized gap 8.8GPT-5.5:58.4%GPT-5.6 Sol:67.2%BrowseCompAgentic
Normalized gap 7.8GPT-5.5:84.4%GPT-5.6 Sol:92.2%
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 v5.7 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
Like-for-like- GPT-5.5
- 60.1
- Supported · #15/105
- GPT-5.6 Sol
- 69.6
- Supported · #7/105
- Basis
- BenchAlign v5.7 lane · 14 vs 9 public rows
- Reading
- GPT-5.6 Sol leads · intervals overlap
Coding
Like-for-like- GPT-5.5
- 64.1
- Supported · #11/135
- GPT-5.6 Sol
- 71.6
- Supported · #6/135
- Basis
- BenchAlign v5.7 lane · 9 vs 12 public rows
- Reading
- GPT-5.6 Sol leads · intervals overlap
Reasoning
Like-for-like- GPT-5.5
- 65.9
- #10/19
- GPT-5.6 Sol
- 72.1
- #8/19
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- GPT-5.6 Sol leads
Knowledge
Like-for-like- GPT-5.5
- 70.0
- Supported · #12/158
- GPT-5.6 Sol
- 78.8
- Supported · #7/158
- Basis
- BenchAlign v5.7 lane · 6 vs 8 public rows
- Reading
- GPT-5.6 Sol leads · intervals overlap
Multimodal
Directional only- GPT-5.5
- 71.4
- #20/50
- GPT-5.6 Sol
- 87.6
- #5/50
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.5
- 91.9
- #7/124
- GPT-5.6 Sol
- 87.7
- #28/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Multilingual
Not comparable- GPT-5.5
- Not ranked
- GPT-5.6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5
- 69.4
- Unranked · 3 rankable rows
- GPT-5.6 Sol
- 96.8
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
| Category | GPT-5.5 | GPT-5.6 Sol | Basis | Reading |
|---|---|---|---|---|
| Agentic | 60.1Supported · #15/105 | 69.6Supported · #7/105 | Like-for-likeBenchAlign v5.7 lane · 14 vs 9 public rows | GPT-5.6 Sol leads · intervals overlap |
| Coding | 64.1Supported · #11/135 | 71.6Supported · #6/135 | Like-for-likeBenchAlign v5.7 lane · 9 vs 12 public rows | GPT-5.6 Sol leads · intervals overlap |
| Reasoning | 65.9#10/19 | 72.1#8/19 | Like-for-likeProvisional lane · 2 vs 2 weighted rows | GPT-5.6 Sol leads |
| Knowledge | 70.0Supported · #12/158 | 78.8Supported · #7/158 | Like-for-likeBenchAlign v5.7 lane · 6 vs 8 public rows | GPT-5.6 Sol leads · intervals overlap |
| Multimodal | 71.4#20/50 | 87.6#5/50 | Directional onlyProvisional lane · 2 vs 1 weighted rows | Directional only |
| Instruction following | 91.9#7/124 | 87.7#28/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 69.4Unranked · 3 rankable rows | 96.8Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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
- GPT-5.5
- $0.02
- Fits in one request
- GPT-5.6 Sol
- $0.014
- Fits in one request
GPT-5.6 Sol has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.5
- $0.34
- Fits in one request
- GPT-5.6 Sol
- $0.26
- Fits in one request
GPT-5.6 Sol 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-5.5
- $0.5
- Fits in one request
- GPT-5.6 Sol
- $0.36
- Fits in one request
GPT-5.6 Sol has the lower modeled cost
Costs use the listed standard API rates.
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.
SpecificationGPT-5.5GPT-5.6 Sol
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5
GPT-5.6 Sol
1.05M
OpenAI model catalogAPI model ID
GPT-5.5
gpt-5.5
OpenAI pricingGPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingGPT-5.6 Sol
$0.4 per 1M cached input tokens
OpenAI pricingDocumented inputs
GPT-5.5
Not sourced
GPT-5.6 Sol
text, image
OpenAI model catalogDocumented outputs
GPT-5.5
Not sourced
GPT-5.6 Sol
Provider availability
GPT-5.5
Not sourced
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GPT-5.5
Reasoning
GPT-5.6 Sol
Reasoning
Weight access
GPT-5.5
Proprietary
GPT-5.6 Sol
Proprietary
License
GPT-5.5
Proprietary
GPT-5.6 Sol
Proprietary
Release date
GPT-5.5
2026-04-23
GPT-5.6 Sol
2026-07-09
If you are considering the documented upgrade path
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GPT-5.6 Sol has the higher public score, 78.49 versus 69.13, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.34 vs $0.26. Cache-heavy agent loop: $0.5 vs $0.36.
- Context tradeoff
- GPT-5.6 Sol has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.5 or GPT-5.6 Sol?
GPT-5.6 Sol has the higher public score, 78.49 versus 69.13, 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, GPT-5.5 or GPT-5.6 Sol?
GPT-5.6 Sol leads the public coding lane, 71.6 to 64.1, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GPT-5.5 or GPT-5.6 Sol?
GPT-5.6 Sol leads the public agentic tasks lane, 69.6 to 60.1, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, GPT-5.5 or GPT-5.6 Sol?
For the stated presets, chat costs $0.02 on GPT-5.5 and $0.014 on GPT-5.6 Sol; repository review costs $0.34 and $0.26; the cache-heavy agent loop costs $0.5 and $0.36. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.5 or GPT-5.6 Sol?
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 1M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence54 rows
Agentic
Terminal-Bench 2.0
GPT-5.582%SourceGPT-5.6 Sol—Not directly comparable
CyberGym
GPT-5.581.8%SourceGPT-5.6 Sol84.5%SourceGPT-5.6 Sol leads this result
BrowseComp
GPT-5.584.4%SourceGPT-5.6 Sol92.2%SourceGPT-5.6 Sol leads this result
OSWorld-Verified
GPT-5.578.7%SourceGPT-5.6 Sol—Not directly comparable
MCP Atlas
GPT-5.575.3%SourceGPT-5.6 Sol—Not directly comparable
Toolathlon
GPT-5.555.6%SourceGPT-5.6 Sol58%SourceGPT-5.6 Sol leads this result
τ²-bench results
GPT-5.598%SourceGPT-5.6 Sol—Not directly comparable
Gert Labs
GPT-5.572.93%SourceGPT-5.6 Sol—Not directly comparable
ResearchClawBench
GPT-5.517.0%SourceGPT-5.6 Sol—Not directly comparable
OSWorld 2.0
GPT-5.513.0%SourceGPT-5.6 Sol62.6%SourceGPT-5.6 Sol leads this result
JobBench
GPT-5.542.7%SourceGPT-5.6 Sol—Not directly comparable
ExploitGym
GPT-5.513.4%SourceGPT-5.6 Sol33.7%SourceGPT-5.6 Sol leads this result
Terminal-Bench 2.1 (Vals)
GPT-5.576.4%SourceGPT-5.6 Sol85.8%SourceGPT-5.6 Sol leads this result
ApprenticeBench
Shared sourceGPT-5.520%GPT-5.6 Sol26%GPT-5.6 Sol leads this result
Terminal-Bench 3.0
GPT-5.5—GPT-5.6 Sol34.6%SourceNot directly comparable
Terminal-Bench 2.1
GPT-5.5—GPT-5.6 Sol91.9%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.558.6%SourceGPT-5.6 Sol64.6%SourceGPT-5.6 Sol leads this result
Terminal-Bench 2.0
GPT-5.582.0%SourceGPT-5.6 Sol—Not directly comparable
Vibe Code Bench
GPT-5.569.85%SourceGPT-5.6 Sol—Not directly comparable
React Native Evals
GPT-5.584.7%SourceGPT-5.6 Sol—Not directly comparable
cursorBench31
GPT-5.559.2%SourceGPT-5.6 Sol—Not directly comparable
cursorBench32
Shared sourceGPT-5.558.4%GPT-5.6 Sol67.2%GPT-5.6 Sol leads this result
FrontierCode 1.1 Main
GPT-5.543.0%SourceGPT-5.6 Sol—Not directly comparable
LiveCodeBench (Vals)
GPT-5.585.3%SourceGPT-5.6 Sol82.6%SourceGPT-5.5 leads this result
SWE-bench (Vals)
GPT-5.582.6%SourceGPT-5.6 Sol96.2%SourceGPT-5.6 Sol leads this result
Bug Hunt Bench
GPT-5.5—GPT-5.6 Sol42 fixesSourceNot directly comparable
Terminal-Bench 2.1
GPT-5.5—GPT-5.6 Sol91.9%SourceNot directly comparable
DeepSWE
GPT-5.5—GPT-5.6 Sol72.7%SourceNot directly comparable
FrontierCode 1.1 Extended
GPT-5.5—GPT-5.6 Sol60.6%SourceNot directly comparable
FrontierSWE v2
GPT-5.5—GPT-5.6 Sol32.2%SourceNot directly comparable
VulcanBench v3
GPT-5.5—GPT-5.6 Sol87.0%SourceNot directly comparable
VulcanBench CII v1
GPT-5.5—GPT-5.6 Sol86.5%SourceNot directly comparable
cursorBench40
GPT-5.5—GPT-5.6 Sol41.7%SourceNot directly comparable
Reasoning
MRCR v2 64K-128K
GPT-5.583.1%SourceGPT-5.6 Sol—Not directly comparable
MRCR v2 128K-256K
GPT-5.587.5%SourceGPT-5.6 Sol—Not directly comparable
ARC-AGI-2
GPT-5.585%SourceGPT-5.6 Sol92.5%SourceGPT-5.6 Sol leads this result
ARC-AGI-3
GPT-5.50.4%SourceGPT-5.6 Sol7.8%SourceGPT-5.6 Sol leads this result
GeneBench-Pro
GPT-5.5—GPT-5.6 Sol28.7%SourceNot directly comparable
Multimodal
Knowledge
GPQA
GPT-5.593.6%SourceGPT-5.6 Sol94.6%SourceGPT-5.6 Sol leads this result
GPQA-D
GPT-5.593.6%SourceGPT-5.6 Sol94.6%SourceGPT-5.6 Sol leads this result
HLE
GPT-5.552.2%SourceGPT-5.6 Sol—Not directly comparable
HLE w/o tools
GPT-5.541.4%SourceGPT-5.6 Sol—Not directly comparable
GPQA Diamond (Vals)
GPT-5.593.2%SourceGPT-5.6 Sol95.2%SourceGPT-5.6 Sol leads this result
MMLU-Pro (Vals)
GPT-5.588.1%SourceGPT-5.6 Sol89.1%SourceGPT-5.6 Sol leads this result
HLE-Verified
GPT-5.5—GPT-5.6 Sol54.5%SourceNot directly comparable
LABBench2
GPT-5.5—GPT-5.6 Sol82.1%SourceNot directly comparable
HealthBench Professional
GPT-5.5—GPT-5.6 Sol60.5%SourceNot directly comparable
HealthBench Hard
GPT-5.5—GPT-5.6 Sol33.1%SourceNot directly comparable
Math
54 public results · 22 shared
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Last updated September 24, 2026