Coding work
Code generation, repair, and software-engineering tasks
GPT-6.1 Sol
GPT-6.1 Sol leads on the public coding lane, 67 to 63, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
GPT-6.1 Sol vs GPT-6 Sol
Updated September 29, 2026. Rank says GPT-6 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty. This is a same-family comparison, so migration details appear when the source data supports them.
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-6 Sol has the higher public score estimate, 78.57 versus 66.86, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 8 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 8
- GPT-6.1 Sol only
- 1
- GPT-6 Sol only
- 2
- Like-for-like categories
- 1 / 8
Estimated: GPT-6.1 Sol and GPT-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.
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-6.1 Sol
GPT-6.1 Sol 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
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6.1 Sol is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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-ratesRepository 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
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.
67.0GPT-6.1 Sol63.0GPT-6 Sol
Like-for-like · BenchAlign v5.7
GPT-6.1 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.
1 GPT-6.1 Sol only
8 Shared
2 GPT-6 Sol only
1 category rests 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.
AutomationBenchAgentic
Normalized gap 2.9GPT-6.1 Sol:36.1%GPT-6 Sol:33.2%
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.
Coding
Like-for-like- GPT-6.1 Sol
- 67.0
- Supported · #8/143
- GPT-6 Sol
- 63.0
- Supported · #13/143
- Basis
- BenchAlign v5.7 lane · 1 vs 1 public rows
- Reading
- GPT-6.1 Sol leads · intervals overlap
Knowledge
Directional only- GPT-6.1 Sol
- 71.3
- Estimated · #10/169
- GPT-6 Sol
- 78.5
- Supported · #7/169
- Basis
- BenchAlign v5.7 lane · 5 vs 5 public rows
- Reading
- Directional only
Agentic
Not comparable- GPT-6.1 Sol
- Not ranked
- GPT-6 Sol
- 59.9
- Supported · #19/117
- Basis
- BenchAlign v5.7 lane · 3 vs 4 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-6.1 Sol
- 79.4
- Unranked · 2 rankable rows
- GPT-6 Sol
- 79.6
- #4/27
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-6.1 Sol
- 83.9
- Unranked · 1 rankable row
- GPT-6 Sol
- 83.8
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-6.1 Sol
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-6.1 Sol
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-6.1 Sol
- Not ranked
- GPT-6 Sol
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-6.1 Sol | GPT-6 Sol | Basis | Reading |
|---|---|---|---|---|
| Coding | 67.0Supported · #8/143 | 63.0Supported · #13/143 | Like-for-likeBenchAlign v5.7 lane · 1 vs 1 public rows | GPT-6.1 Sol leads · intervals overlap |
| Knowledge | 71.3Estimated · #10/169 | 78.5Supported · #7/169 | Directional onlyBenchAlign v5.7 lane · 5 vs 5 public rows | Directional only |
| Agentic | Not ranked | 59.9Supported · #19/117 | Not comparableBenchAlign v5.7 lane · 3 vs 4 public rows | Not comparable |
| Reasoning | 79.4Unranked · 2 rankable rows | 79.6#4/27 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 83.9Unranked · 1 rankable row | 83.8Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 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-6.1 Sol
- $0.007
- Fits in one request
- GPT-6 Sol
- $0.007
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-6.1 Sol
- $0.13
- Fits in one request
- GPT-6 Sol
- $0.13
- 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-6.1 Sol
- $0.16
- Fits in one request
- GPT-6 Sol
- $0.18
- Fits in one request
GPT-6.1 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-6.1 SolGPT-6 Sol
Context window
Maximum documented context; output-token limits may be lower.
GPT-6.1 Sol
GPT-6 Sol
API model ID
GPT-6.1 Sol
gpt-6.1-sol
OpenAI GPT-6.1 Sol model documentationGPT-6 Sol
gpt-6-sol
OpenAI GPT-6 Sol model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-6.1 Sol
$0.1 per 1M cached input tokens
OpenAI GPT-6.1 Sol model documentationGPT-6 Sol
$0.2 per 1M cached input tokens
OpenAI GPT-6 Sol model documentationDocumented inputs
GPT-6.1 Sol
text, image
OpenAI GPT-6.1 Sol model documentationGPT-6 Sol
text, image
OpenAI GPT-6 Sol model documentationDocumented outputs
GPT-6.1 Sol
GPT-6 Sol
Provider availability
GPT-6.1 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API
OpenAI GPT-6.1 Sol model documentationGPT-6 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex
OpenAI GPT-6 Sol and Luna launchReasoning profile
GPT-6.1 Sol
Reasoning
GPT-6 Sol
Reasoning
Weight access
GPT-6.1 Sol
Proprietary
GPT-6 Sol
Proprietary
License
GPT-6.1 Sol
Proprietary
GPT-6 Sol
Proprietary
Release date
GPT-6.1 Sol
2026-09-29
GPT-6 Sol
2026-09-22
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-6 Sol has the higher public score estimate, 78.57 versus 66.86, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.13 vs $0.13. Cache-heavy agent loop: $0.16 vs $0.18.
- Context tradeoff
- Both models list 1.05M.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-6.1 Sol or GPT-6 Sol?
GPT-6 Sol has the higher public score estimate, 78.57 versus 66.86, 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-6.1 Sol or GPT-6 Sol?
GPT-6.1 Sol leads the public coding lane, 67 to 63, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, GPT-6.1 Sol or GPT-6 Sol?
GPT-6.1 Sol is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-6.1 Sol or GPT-6 Sol?
For the stated presets, chat costs $0.007 on GPT-6.1 Sol and $0.007 on GPT-6 Sol; repository review costs $0.13 and $0.13; the cache-heavy agent loop costs $0.16 and $0.18. Costs use the listed standard API rates.
Which has the larger context window, GPT-6.1 Sol or GPT-6 Sol?
Both models list the same context window, 1.05M.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence11 rows
Agentic
AutomationBench
GPT-6.1 Sol36.1%SourceGPT-6 Sol33.2%SourceGPT-6.1 Sol leads this result
Terminal-Bench-Science 0.1
GPT-6.1 Sol57.0%SourceGPT-6 Sol—Not directly comparable
ExploitGym
GPT-6.1 Sol35.1%SourceGPT-6 Sol22.1%SourceGPT-6.1 Sol leads this result
Agents' Last Exam
GPT-6.1 Sol—GPT-6 Sol56.4%SourceNot directly comparable
OSWorld 2.0
GPT-6.1 Sol—GPT-6 Sol60.5%SourceNot directly comparable
Coding
Knowledge
HealthBench (raw)
GPT-6.1 Sol56.7%SourceGPT-6 Sol47.1%SourceGPT-6.1 Sol leads this result
HealthBench (length-adjusted)
GPT-6.1 Sol58.5%SourceGPT-6 Sol53.2%SourceGPT-6.1 Sol leads this result
HealthBench Professional
GPT-6.1 Sol64.2%SourceGPT-6 Sol60.8%SourceGPT-6.1 Sol leads this result
HealthBench Professional (raw)
GPT-6.1 Sol67.2%SourceGPT-6 Sol59.5%SourceGPT-6.1 Sol leads this result
HealthBench Hard
GPT-6.1 Sol36.2%SourceGPT-6 Sol30.1%SourceGPT-6.1 Sol leads this result
11 public results · 8 shared
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Last updated September 29, 2026