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 24.2, with Supported evidence for both models and non-overlapping 90% intervals.
Confidence: stronger
GPT-5.6 Sol vs Laguna XS.2
Updated September 24, 2026. We do not rank this pair: at least one has no public score. 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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 6
- GPT-5.6 Sol only
- 31
- Laguna XS.2 only
- 4
- Like-for-like categories
- 1 / 8
Supported: 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.
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
Laguna XS.2 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
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.
71.6GPT-5.6 Sol24.2Laguna XS.2
Like-for-like · BenchAlign v5.7
GPT-5.6 Sol leads the like-for-like coding row.
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.
31 GPT-5.6 Sol only
6 Shared
4 Laguna XS.2 only
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.
MMLU-Pro (Vals)Knowledge
Normalized gap 20.0GPT-5.6 Sol:89.1%Laguna XS.2:69.1%SWE-bench ProCoding
Normalized gap 18.3GPT-5.6 Sol:64.6%Laguna XS.2:46.3%LiveCodeBench (Vals)Coding
Normalized gap 14.8GPT-5.6 Sol:82.6%Laguna XS.2:67.8%
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.
Coding
Like-for-like- GPT-5.6 Sol
- 71.6
- Supported · #6/135
- Laguna XS.2
- 24.2
- Supported · #103/135
- Basis
- BenchAlign v5.7 lane · 12 vs 6 public rows
- Reading
- GPT-5.6 Sol leads
Agentic
Not comparable- GPT-5.6 Sol
- 69.6
- Supported · #7/105
- Laguna XS.2
- Not ranked
- Basis
- BenchAlign v5.7 lane · 9 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.6 Sol
- 72.1
- #8/19
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.6 Sol
- 87.6
- #5/50
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.6 Sol
- 78.8
- Supported · #7/158
- Laguna XS.2
- Not ranked
- Basis
- BenchAlign v5.7 lane · 8 vs 2 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Sol
- Not ranked
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Sol
- 87.7
- #28/124
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Sol
- 96.8
- Unranked · 3 rankable rows
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.6 Sol | Laguna XS.2 | Basis | Reading |
|---|---|---|---|---|
| Coding | 71.6Supported · #6/135 | 24.2Supported · #103/135 | Like-for-likeBenchAlign v5.7 lane · 12 vs 6 public rows | GPT-5.6 Sol leads |
| Agentic | 69.6Supported · #7/105 | Not ranked | Not comparableBenchAlign v5.7 lane · 9 vs 2 public rows | Not comparable |
| Reasoning | 72.1#8/19 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 87.6#5/50 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 78.8Supported · #7/158 | Not ranked | Not comparableBenchAlign v5.7 lane · 8 vs 2 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 87.7#28/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 96.8Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 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-5.6 Sol
- $0.014
- Fits in one request
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
Laguna XS.2 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Sol
- $0.26
- Fits in one request
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
Laguna XS.2 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.6 Sol
- $0.36
- Fits in one request
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Laguna XS.2 has no comparable published API token 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.
SpecificationGPT-5.6 SolLaguna XS.2
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Sol
1.05M
OpenAI model catalogLaguna XS.2
256K
API model ID
GPT-5.6 Sol
gpt-5.6-sol
OpenAI model catalogLaguna XS.2
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Sol
$0.4 per 1M cached input tokens
OpenAI pricingLaguna XS.2
No comparable hosted API rate
Documented inputs
GPT-5.6 Sol
text, image
OpenAI model catalogLaguna XS.2
Not sourced
Documented outputs
GPT-5.6 Sol
Laguna XS.2
Not sourced
Provider availability
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogLaguna XS.2
Not sourced
Reasoning profile
GPT-5.6 Sol
Reasoning
Laguna XS.2
Reasoning
Weight access
GPT-5.6 Sol
Proprietary
Laguna XS.2
Open Weight
License
GPT-5.6 Sol
Proprietary
Laguna XS.2
Open Weight
Release date
GPT-5.6 Sol
2026-07-09
Laguna XS.2
2026-04-28
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- 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-5.6 Sol or Laguna XS.2?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.6 Sol or Laguna XS.2?
GPT-5.6 Sol leads the public coding lane, 71.6 to 24.2, with Supported evidence for both models and non-overlapping 90% intervals.
Which is better for agentic tasks, GPT-5.6 Sol or Laguna XS.2?
Laguna XS.2 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.6 Sol or Laguna XS.2?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GPT-5.6 Sol or Laguna XS.2?
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 256K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence41 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Sol34.6%SourceLaguna XS.2—Not directly comparable
Terminal-Bench 2.1
GPT-5.6 Sol91.9%SourceLaguna XS.2—Not directly comparable
BrowseComp
GPT-5.6 Sol92.2%SourceLaguna XS.2—Not directly comparable
OSWorld 2.0
GPT-5.6 Sol62.6%SourceLaguna XS.2—Not directly comparable
CyberGym
GPT-5.6 Sol84.5%SourceLaguna XS.2—Not directly comparable
ExploitGym
GPT-5.6 Sol33.7%SourceLaguna XS.2—Not directly comparable
Toolathlon
GPT-5.6 Sol58%SourceLaguna XS.2—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Sol85.8%SourceLaguna XS.225.8%SourceGPT-5.6 Sol leads this result
ApprenticeBench
GPT-5.6 Sol26%SourceLaguna XS.2—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Sol—Laguna XS.235.7%SourceNot directly comparable
Coding
Bug Hunt Bench
GPT-5.6 Sol42 fixesSourceLaguna XS.2—Not directly comparable
SWE-bench Pro
GPT-5.6 Sol64.6%SourceLaguna XS.246.3%SourceGPT-5.6 Sol leads this result
Terminal-Bench 2.1
GPT-5.6 Sol91.9%SourceLaguna XS.2—Not directly comparable
DeepSWE
GPT-5.6 Sol72.7%SourceLaguna XS.2—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Sol60.6%SourceLaguna XS.2—Not directly comparable
FrontierSWE v2
GPT-5.6 Sol32.2%SourceLaguna XS.2—Not directly comparable
cursorBench32
GPT-5.6 Sol67.2%SourceLaguna XS.2—Not directly comparable
VulcanBench v3
GPT-5.6 Sol87.0%SourceLaguna XS.2—Not directly comparable
VulcanBench CII v1
GPT-5.6 Sol86.5%SourceLaguna XS.2—Not directly comparable
LiveCodeBench (Vals)
GPT-5.6 Sol82.6%SourceLaguna XS.267.8%SourceGPT-5.6 Sol leads this result
SWE-bench (Vals)
GPT-5.6 Sol96.2%SourceLaguna XS.255.2%SourceGPT-5.6 Sol leads this result
cursorBench40
GPT-5.6 Sol41.7%SourceLaguna XS.2—Not directly comparable
SWE-bench Verified
GPT-5.6 Sol—Laguna XS.269.9%SourceNot directly comparable
SWE Multilingual
GPT-5.6 Sol—Laguna XS.257.7%SourceNot directly comparable
Terminal-Bench 2.0
GPT-5.6 Sol—Laguna XS.235.7%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA
GPT-5.6 Sol94.6%SourceLaguna XS.2—Not directly comparable
GPQA-D
GPT-5.6 Sol94.6%SourceLaguna XS.2—Not directly comparable
HLE-Verified
GPT-5.6 Sol54.5%SourceLaguna XS.2—Not directly comparable
LABBench2
GPT-5.6 Sol82.1%SourceLaguna XS.2—Not directly comparable
HealthBench Professional
GPT-5.6 Sol60.5%SourceLaguna XS.2—Not directly comparable
HealthBench Hard
GPT-5.6 Sol33.1%SourceLaguna XS.2—Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Sol95.2%SourceLaguna XS.255.1%SourceGPT-5.6 Sol leads this result
MMLU-Pro (Vals)
GPT-5.6 Sol89.1%SourceLaguna XS.269.1%SourceGPT-5.6 Sol leads this result
Math
41 public results · 6 shared
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Last updated September 24, 2026