Chat turn cost
1K fresh input + 500 output tokens
Pareto 26.9
Pareto 26.9 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
GPT-6.1 Sol vs Pareto 26.9
Updated September 29, 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
Share or export
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. 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-6.1 Sol only
- 8
- Pareto 26.9 only
- 3
- Like-for-like categories
- 0 / 8
Estimated: GPT-6.1 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
Repository review cost
50K fresh input + 3K 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-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Pareto 26.9 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6.1 Sol and Pareto 26.9 are 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
Not enough matched evidence
A complete context comparison is not sourced.
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.
67.0GPT-6.1 Sol—Pareto 26.9
Not comparable · BenchAlign v5.7
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
8 GPT-6.1 Sol only
1 Shared
3 Pareto 26.9 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
Not comparable- GPT-6.1 Sol
- Not ranked
- Pareto 26.9
- Not ranked
- Basis
- BenchAlign v5.7 lane · 3 vs 1 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-6.1 Sol
- 67.0
- Supported · #8/143
- Pareto 26.9
- Not ranked
- Basis
- BenchAlign v5.7 lane · 1 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-6.1 Sol
- 79.4
- Unranked · 2 rankable rows
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-6.1 Sol
- 83.9
- Unranked · 1 rankable row
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-6.1 Sol
- 71.3
- Estimated · #10/169
- Pareto 26.9
- Not ranked
- Basis
- BenchAlign v5.7 lane · 5 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-6.1 Sol
- Not ranked
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-6.1 Sol
- Not ranked
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-6.1 Sol
- Not ranked
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-6.1 Sol | Pareto 26.9 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.7 lane · 3 vs 1 public rows | Not comparable |
| Coding | 67.0Supported · #8/143 | Not ranked | Not comparableBenchAlign v5.7 lane · 1 vs 1 public rows | Not comparable |
| Reasoning | 79.4Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 83.9Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | 71.3Estimated · #10/169 | Not ranked | Not comparableBenchAlign v5.7 lane · 5 vs 1 public 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
- Pareto 26.9
- $0.00625
- Fit state unavailable
Pareto 26.9 has the lower modeled cost
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
- Pareto 26.9
- $0.1475
- Fit state unavailable
GPT-6.1 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-6.1 Sol
- $0.16
- Fits in one request
- Pareto 26.9
- $0.175
- Fit state unavailable
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 SolPareto 26.9
Context window
Maximum documented context; output-token limits may be lower.
GPT-6.1 Sol
Pareto 26.9
N/A
API model ID
GPT-6.1 Sol
gpt-6.1-sol
OpenAI GPT-6.1 Sol model documentationPareto 26.9
pareto
Unbiased pricingCached-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 documentationPareto 26.9
$0.25 per 1M cached input tokens
Unbiased pricingDocumented inputs
GPT-6.1 Sol
text, image
OpenAI GPT-6.1 Sol model documentationPareto 26.9
Not sourced
Documented outputs
GPT-6.1 Sol
Pareto 26.9
Not sourced
Provider availability
GPT-6.1 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API
OpenAI GPT-6.1 Sol model documentationPareto 26.9
Not sourced
Reasoning profile
GPT-6.1 Sol
Reasoning
Pareto 26.9
Reasoning
Weight access
GPT-6.1 Sol
Proprietary
Pareto 26.9
Proprietary
License
GPT-6.1 Sol
Proprietary
Pareto 26.9
Proprietary
Release date
GPT-6.1 Sol
2026-09-29
Pareto 26.9
2026-09-17
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
- Repository review: $0.13 vs $0.1475. Cache-heavy agent loop: $0.16 vs $0.175.
- Context tradeoff
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-6.1 Sol or Pareto 26.9?
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-6.1 Sol or Pareto 26.9?
Pareto 26.9 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-6.1 Sol or Pareto 26.9?
GPT-6.1 Sol and Pareto 26.9 are 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 Pareto 26.9?
For the stated presets, chat costs $0.007 on GPT-6.1 Sol and $0.00625 on Pareto 26.9; repository review costs $0.13 and $0.1475; the cache-heavy agent loop costs $0.16 and $0.175. Costs use the listed standard API rates.
Which has the larger context window, GPT-6.1 Sol or Pareto 26.9?
A complete documented context-window comparison is not available.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence12 rows
Agentic
AutomationBench
GPT-6.1 Sol36.1%SourcePareto 26.9—Not directly comparable
Terminal-Bench-Science 0.1
GPT-6.1 Sol57.0%SourcePareto 26.9—Not directly comparable
ExploitGym
GPT-6.1 Sol35.1%SourcePareto 26.9—Not directly comparable
Terminal-Bench 4.0
GPT-6.1 Sol—Pareto 26.951.00%SourceNot directly comparable
Coding
Multimodal
MMMU-Pro
GPT-6.1 Sol—Pareto 26.978%SourceNot directly comparable
Knowledge
HealthBench (raw)
GPT-6.1 Sol56.7%SourcePareto 26.9—Not directly comparable
HealthBench (length-adjusted)
GPT-6.1 Sol58.5%SourcePareto 26.9—Not directly comparable
HealthBench Professional
GPT-6.1 Sol64.2%SourcePareto 26.9—Not directly comparable
HealthBench Professional (raw)
GPT-6.1 Sol67.2%SourcePareto 26.9—Not directly comparable
HealthBench Hard
GPT-6.1 Sol36.2%SourcePareto 26.9—Not directly comparable
HLE w/o tools
GPT-6.1 Sol—Pareto 26.949%SourceNot directly comparable
12 public results · 1 shared
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Last updated September 29, 2026