Coding
Like-for-like- GPT-5.5
- 58.6
- GPT-5.6 Sol
- 64.6
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-5.6 Sol leads
Model comparison
GPT-5.5 vs GPT-5.6 Sol
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
GPT-5.6 Sol has the higher public score estimate, 81.48 versus 72.31, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
18 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.
Coding work
Code generation, repair, and software-engineering tasks
GPT-5.6 Sol
GPT-5.6 Sol leads on the same 1 weighted benchmark row.
Confidence: limited
Long 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
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
Chat turn cost
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
15 GPT-5.5 only
18 Shared
6 GPT-5.6 Sol only
- Shared results
- 18
- GPT-5.5 only
- 15
- GPT-5.6 Sol only
- 6
- Like-for-like categories
- 3 / 8
3 categories use 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.
Reasoning
Like-for-like- GPT-5.5
- 85.0
- GPT-5.6 Sol
- 92.5
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-5.6 Sol leads
Math
Like-for-like- GPT-5.5
- 47.6
- GPT-5.6 Sol
- 87.5
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.6 Sol leads
Agentic
Directional only- GPT-5.5
- 81.6
- GPT-5.6 Sol
- 92.0
- Weighted basis
- 3 vs 2 rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.5
- 57.8
- GPT-5.6 Sol
- 94.6
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Multimodal
Directional only- GPT-5.5
- 70.4
- GPT-5.6 Sol
- 83.0
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Multilingual
Not comparable- GPT-5.5
- Not measured
- GPT-5.6 Sol
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.5
- Not measured
- GPT-5.6 Sol
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.5 | GPT-5.6 Sol | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 58.6 | 64.6 | Like-for-like1 vs 1 rows | GPT-5.6 Sol leads |
| Reasoning | 85.0 | 92.5 | Like-for-like1 vs 1 rows | GPT-5.6 Sol leads |
| Math | 47.6 | 87.5 | Like-for-like2 vs 2 rows | GPT-5.6 Sol leads |
| Agentic | 81.6 | 92.0 | Directional only3 vs 2 rows | Directional only |
| Knowledge | 57.8 | 94.6 | Directional only2 vs 1 rows | Directional only |
| Multimodal | 70.4 | 83.0 | Directional only2 vs 1 rows | Directional only |
| 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.
FrontierMath v2 (Tier 4)
Math
FrontierMath v2 (Tiers 1-3)
Math
Terminal-Bench 2.0
Agentic
BrowseComp
Agentic
ARC-AGI-2
Reasoning
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
- $0.02
- Fits in one request
Modeled costs are equal
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
- $0.34
- Fits in one request
Modeled costs are equal
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
- $0.5
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
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
$0.5 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 estimate, 81.48 versus 72.31, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.34 vs $0.34. Cache-heavy agent loop: $0.5 vs $0.5.
- 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.
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
Terminal-Bench 2.0
GPT-5.582%SourceGPT-5.6 Sol91.9%SourceGPT-5.6 Sol leads this result
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
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 Sol91.9%SourceGPT-5.6 Sol leads this result
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
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
VulcanBench v3
GPT-5.5—GPT-5.6 Sol87.0%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
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
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
Multimodal
Frequently asked questions
Which is better, GPT-5.5 or GPT-5.6 Sol?
GPT-5.6 Sol has the higher public score estimate, 81.48 versus 72.31, 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-5.5 or GPT-5.6 Sol?
GPT-5.6 Sol leads the like-for-like coding comparison across 1 shared weighted benchmark row.
Which is better for agentic tasks, GPT-5.5 or GPT-5.6 Sol?
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-5.5 or GPT-5.6 Sol?
For the stated presets, chat costs $0.02 on GPT-5.5 and $0.02 on GPT-5.6 Sol; repository review costs $0.34 and $0.34; the cache-heavy agent loop costs $0.5 and $0.5. 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.
Related comparisons
Last updated August 10, 2026
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