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
GPT-5.1-Codex 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 44.47, and the 90% score intervals do not overlap. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- GPT-5.1-Codex only
- 3
- GPT-5.6 Sol only
- 36
- Like-for-like categories
- 0 / 8
Estimated: GPT-5.1-Codex · 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.
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.1-Codex
GPT-5.1-Codex 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-5.1-Codex
GPT-5.1-Codex 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
GPT-5.1-Codex
GPT-5.1-Codex 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
GPT-5.1-Codex 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
GPT-5.1-Codex is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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.
33.1GPT-5.1-Codex71.6GPT-5.6 Sol
Directional only · BenchAlign v5.7
GPT-5.6 Sol scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
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.
3 GPT-5.1-Codex only
1 Shared
36 GPT-5.6 Sol only
3 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.
LiveCodeBench (Vals)Coding
Normalized gap 3.0GPT-5.1-Codex:85.6%GPT-5.6 Sol:82.6%
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
Directional only- GPT-5.1-Codex
- 33.1
- Estimated · #78/135
- GPT-5.6 Sol
- 71.6
- Supported · #6/135
- Basis
- BenchAlign v5.7 lane · 2 vs 12 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.1-Codex
- 48.2
- Estimated · #63/158
- GPT-5.6 Sol
- 78.8
- Supported · #7/158
- Basis
- BenchAlign v5.7 lane · 0 vs 8 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.1-Codex
- 84.2
- #42/124
- GPT-5.6 Sol
- 87.7
- #28/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.1-Codex
- Not ranked
- GPT-5.6 Sol
- 69.6
- Supported · #7/105
- Basis
- BenchAlign v5.7 lane · 2 vs 9 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.1-Codex
- 69.7
- Unranked · 2 rankable rows
- GPT-5.6 Sol
- 72.1
- #8/19
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.1-Codex
- 66.9
- Unranked · 1 rankable row
- GPT-5.6 Sol
- 87.6
- #5/50
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.1-Codex
- Not ranked
- GPT-5.6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.1-Codex
- Not ranked
- GPT-5.6 Sol
- 96.8
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | GPT-5.1-Codex | GPT-5.6 Sol | Basis | Reading |
|---|---|---|---|---|
| Coding | 33.1Estimated · #78/135 | 71.6Supported · #6/135 | Directional onlyBenchAlign v5.7 lane · 2 vs 12 public rows | Directional only |
| Knowledge | 48.2Estimated · #63/158 | 78.8Supported · #7/158 | Directional onlyBenchAlign v5.7 lane · 0 vs 8 public rows | Directional only |
| Instruction following | 84.2#42/124 | 87.7#28/124 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 69.6Supported · #7/105 | Not comparableBenchAlign v5.7 lane · 2 vs 9 public rows | Not comparable |
| Reasoning | 69.7Unranked · 2 rankable rows | 72.1#8/19 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multimodal | 66.9Unranked · 1 rankable row | 87.6#5/50 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | 96.8Unranked · 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 v5.7) 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-5.1-Codex
- $0.00625
- Fits in one request
- GPT-5.6 Sol
- $0.014
- Fits in one request
GPT-5.1-Codex has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.1-Codex
- $0.0925
- Fits in one request
- GPT-5.6 Sol
- $0.26
- Fits in one request
GPT-5.1-Codex 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-5.1-Codex
- $0.15
- Fits in one request
- GPT-5.6 Sol
- $0.36
- Fits in one request
GPT-5.1-Codex 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-5.1-CodexGPT-5.6 Sol
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.1-Codex
GPT-5.6 Sol
1.05M
OpenAI model catalogAPI model ID
GPT-5.1-Codex
gpt-5.1-codex
OpenAI GPT-5.1-Codex model documentationGPT-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-5.1-Codex
$0.125 per 1M cached input tokens
OpenAI GPT-5.1-Codex model documentationGPT-5.6 Sol
$0.4 per 1M cached input tokens
OpenAI pricingDocumented inputs
GPT-5.1-Codex
Not sourced
GPT-5.6 Sol
text, image
OpenAI model catalogDocumented outputs
GPT-5.1-Codex
Not sourced
GPT-5.6 Sol
Provider availability
GPT-5.1-Codex
Not sourced
GPT-5.6 Sol
Generally Available · OpenAI Responses API
OpenAI model catalogReasoning profile
GPT-5.1-Codex
Reasoning
GPT-5.6 Sol
Reasoning
Weight access
GPT-5.1-Codex
Proprietary
GPT-5.6 Sol
Proprietary
License
GPT-5.1-Codex
Proprietary
GPT-5.6 Sol
Proprietary
Release date
GPT-5.1-Codex
2025-10-15
GPT-5.6 Sol
2026-07-09
If you already use one of these models
- 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 44.47, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0925 vs $0.26. Cache-heavy agent loop: $0.15 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-5.1-Codex or GPT-5.6 Sol?
GPT-5.6 Sol has the higher public score, 78.49 versus 44.47, 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-5.1-Codex or GPT-5.6 Sol?
GPT-5.6 Sol scores higher for coding on the public lane, 71.6 to 33.1. GPT-5.1-Codex 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, GPT-5.1-Codex or GPT-5.6 Sol?
GPT-5.1-Codex is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.1-Codex or GPT-5.6 Sol?
For the stated presets, chat costs $0.00625 on GPT-5.1-Codex and $0.014 on GPT-5.6 Sol; repository review costs $0.0925 and $0.26; the cache-heavy agent loop costs $0.15 and $0.36. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.1-Codex or GPT-5.6 Sol?
GPT-5.6 Sol has the larger documented context window: 1.05M, compared with 400K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence40 rows
Agentic
Gert Labs
GPT-5.1-Codex49.68%SourceGPT-5.6 Sol—Not directly comparable
JobBench
GPT-5.1-Codex26.2%SourceGPT-5.6 Sol—Not directly comparable
Terminal-Bench 3.0
GPT-5.1-Codex—GPT-5.6 Sol34.6%SourceNot directly comparable
Terminal-Bench 2.1
GPT-5.1-Codex—GPT-5.6 Sol91.9%SourceNot directly comparable
BrowseComp
GPT-5.1-Codex—GPT-5.6 Sol92.2%SourceNot directly comparable
OSWorld 2.0
GPT-5.1-Codex—GPT-5.6 Sol62.6%SourceNot directly comparable
CyberGym
GPT-5.1-Codex—GPT-5.6 Sol84.5%SourceNot directly comparable
ExploitGym
GPT-5.1-Codex—GPT-5.6 Sol33.7%SourceNot directly comparable
Toolathlon
GPT-5.1-Codex—GPT-5.6 Sol58%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.1-Codex—GPT-5.6 Sol85.8%SourceNot directly comparable
ApprenticeBench
GPT-5.1-Codex—GPT-5.6 Sol26%SourceNot directly comparable
Coding
Vibe Code Bench
GPT-5.1-Codex13.12%SourceGPT-5.6 Sol—Not directly comparable
LiveCodeBench (Vals)
GPT-5.1-Codex85.6%SourceGPT-5.6 Sol82.6%SourceGPT-5.1-Codex leads this result
Bug Hunt Bench
GPT-5.1-Codex—GPT-5.6 Sol42 fixesSourceNot directly comparable
SWE-bench Pro
GPT-5.1-Codex—GPT-5.6 Sol64.6%SourceNot directly comparable
Terminal-Bench 2.1
GPT-5.1-Codex—GPT-5.6 Sol91.9%SourceNot directly comparable
DeepSWE
GPT-5.1-Codex—GPT-5.6 Sol72.7%SourceNot directly comparable
FrontierCode 1.1 Extended
GPT-5.1-Codex—GPT-5.6 Sol60.6%SourceNot directly comparable
FrontierSWE v2
GPT-5.1-Codex—GPT-5.6 Sol32.2%SourceNot directly comparable
cursorBench32
GPT-5.1-Codex—GPT-5.6 Sol67.2%SourceNot directly comparable
VulcanBench v3
GPT-5.1-Codex—GPT-5.6 Sol87.0%SourceNot directly comparable
VulcanBench CII v1
GPT-5.1-Codex—GPT-5.6 Sol86.5%SourceNot directly comparable
SWE-bench (Vals)
GPT-5.1-Codex—GPT-5.6 Sol96.2%SourceNot directly comparable
cursorBench40
GPT-5.1-Codex—GPT-5.6 Sol41.7%SourceNot directly comparable
Reasoning
Multimodal
Knowledge
GPQA
GPT-5.1-Codex—GPT-5.6 Sol94.6%SourceNot directly comparable
GPQA-D
GPT-5.1-Codex—GPT-5.6 Sol94.6%SourceNot directly comparable
HLE-Verified
GPT-5.1-Codex—GPT-5.6 Sol54.5%SourceNot directly comparable
LABBench2
GPT-5.1-Codex—GPT-5.6 Sol82.1%SourceNot directly comparable
HealthBench Professional
GPT-5.1-Codex—GPT-5.6 Sol60.5%SourceNot directly comparable
HealthBench Hard
GPT-5.1-Codex—GPT-5.6 Sol33.1%SourceNot directly comparable
GPQA Diamond (Vals)
GPT-5.1-Codex—GPT-5.6 Sol95.2%SourceNot directly comparable
MMLU-Pro (Vals)
GPT-5.1-Codex—GPT-5.6 Sol89.1%SourceNot directly comparable
Math
40 public results · 1 shared
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Last updated September 24, 2026