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BenchLM

Data as of September 29, 2026 · How the score is built

GPT-6.1 Sol

Decision readingGPT-6.1 Sol scores 66.9 out of 100 and ranks #23 of 210. This profile shows 11 source-displayable benchmark rows; its strongest eligible category is Coding at #8. API pricing is $2 input and $10 output per million tokens, with cached input at $0.1.

Released Sep 29, 2026 — see all recent releases

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

66.9/100

field median 50.5#23 of 210 ranked models

Public

#23of 210

Verified —

Price

$2input / $10 output

input median $1cached $0.10 · blended $6

Speed

Not measured

field median 92 tok/sTime to first token not measured

Context

1.05Mtokens

field median 256,000Maximum output length is tracked separately

Strongest published evidence

Coding ranks #8. Particularly well-suited for software development and code generation tasks.

Validate before choosing

11 published rows leave some tracked benchmark slots empty. Independent runtime speed has not been measured.

Source-linked · 11 displayable benchmark rows

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Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank Not rankedWeight 22%3 benchmarksVerified
62.1
CodingRank #8 of 143Percentile 95thWeight 20%1 benchmarkVerified
67.0
ReasoningWeight 17%0 benchmarksNot measured
Not measured
MultimodalWeight 12%0 benchmarksNot measured
Not measured
Knowledge7.2 points below GPT-6 SolRank #10 of 169Percentile 95thWeight 12%5 benchmarksVerified
71.3
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%0 benchmarksNot measured
Not measured
MathWeight 5%0 benchmarksNot measured
Not measured

An earlier release can outscore a newer one in a single category. The note under that category names the release and the gap.

11 of 486 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. Agentic3/3 verified
  2. Coding1/1 verified
  3. ReasoningNot measured
  4. MultimodalNot measured
  5. Knowledge5/5 verified
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  8. MathNot measured
Verified sourceProvisionalNot measured

Capability shape

Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.

GPT-6.1 Sol category percentile values

  • AgenticNot eligible
  • Coding95th percentile
  • ReasoningNot eligible
  • MultimodalNot eligible
  • Knowledge95th percentile
  • MultilingualNot eligible
  • Instruction followingNot eligible
  • MathNot eligible

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. AgenticNot ranked
  2. Coding#8/143
  3. ReasoningNot ranked
  4. MultimodalNot ranked
  5. Knowledge#10/169
  6. MultilingualNot ranked
  7. Inst. FollowingNot ranked
  8. MathNot ranked
Top decileTop quartileMid-fieldNot eligible

Benchmark ledger

Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Coding1 row
Coding benchmark values, best verified comparison, weight, and source status
DeepSWEScore71.9%Versus best verified row

Best verified: Muse Spark 1.3 · 75.4%

Gap3.5 behindWeight15% ref. weight
Agentic3 rows
Agentic benchmark values, best verified comparison, weight, and source status
AutomationBenchScore36.1%Versus best verified row

Best verified: DeepSeek V4.1 Flash · 54.8%

Gap18.7 behindWeight5% ref. weight
Terminal-Bench-Science 0.1Score57.0%Versus best verified row

Best verified: GPT-6 Astra · 64.6%

Gap7.6 behindWeightDisplay only
ExploitGymScore35.1%Versus best verified row

Best verified: GPT-6 Astra · 42.4%

Gap7.3 behindWeightDisplay only
Knowledge5 rows
Knowledge benchmark values, best verified comparison, weight, and source status
HealthBench (raw)HealthBench raw scoreScore56.7%Versus best verified row

Best verified: Claude Sonnet 5.5 · 69.4%

Gap12.7 behindWeightDisplay only
HealthBench (length-adjusted)HealthBench length-adjusted scoreScore58.5%Versus best verified row

Best verified: Claude Sonnet 5.5 · 65.4%

Gap6.9 behindWeightDisplay only
HealthBench ProfessionalScore64.2%Versus best verified row

Best verified: Claude Sonnet 5.5 · 69.2%

Gap5 behindWeightDisplay only
HealthBench Professional (raw)HealthBench Professional raw scoreScore67.2%Versus best verified row

Best verified: Claude Sonnet 5.5 · 77.1%

Gap9.9 behindWeightDisplay only
HealthBench HardScore36.2%Versus best verified row

Best verified: Muse Spark · 42.8%

Gap6.6 behindWeightDisplay only
External signals2 rows
External signals benchmark values, best verified comparison, weight, and source status
ExploitBenchExploitBench v8-benchScore100%Versus best verified row

Best verified: GPT-6 Astra · 100%

Gap0.3 behindWeightDisplay only
SEC-Bench ProScore78.8%Versus best verified row

Best verified: GPT-6 Astra · 85.4%

Gap6.6 behindWeightDisplay only

What it costs to get this score

Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.

Current modelExplore all models

GPT-6.1 Sol · 66.9 score · $6 blended per million tokens

The chart opens on the current model. Scroll horizontally to inspect the full price axis.

2030405060708090100$0.10$0.50$1$5$10$25$50$100↘ frontierGPT-6.1 Sol

Horizontal: blended price per million tokens, log scale · Vertical: public score

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Apr 23, 2026

    GPT-5.5

    Score 68.8 · 1.9 points below GPT-5.4 Pro · $5 / $30

  2. Jul 9, 2026

    GPT-5.6 Sol

    Score 78.2 · $4 / $20

  3. Sep 22, 2026

    GPT-6 Sol

    Score 78.6 · $2 / $10

  4. Sep 29, 2026 · you are here

    GPT-6.1 Sol

    Score 66.9 · 11.7 points below GPT-6 Sol · $2 / $10

Radar

GPT-6.1 Sol release history

Full release history

Radar confirmed these at the source. Use GPT-6.1 Sol in your work? Explore Radar to follow supported changes and choose your alerts.

Radar

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

Maximum output
128,000 tokensOpenAI GPT-6.1 Sol model documentation
Knowledge cutoff
April 30, 2026OpenAI GPT-6.1 Sol model documentation
Input modalities
text, imageOpenAI GPT-6.1 Sol model documentation
Parameters
Not disclosed by the provider
Availability
OpenAI Responses API · OpenAI Chat Completions APIOpenAI GPT-6.1 Sol model documentation

Use the Responses API for tool calling. Chat Completions supports GPT-6.1 Sol without tool calling. OpenAI GPT-6.1 Sol model documentation, read Sep 29, 2026

Cloud regions
Not tracked yet
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Published at $0.10 per million cached input tokensOpenAI GPT-6.1 Sol model documentation
Self-host
Weights are not published
Rate limits
Not tracked yet

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

GPT-6.1 Sol ranks #23 of 210 on the public leaderboard with a score of 66.86/100. It does not yet have enough sourced coverage for a verified position.

GPT-6.1 Sol is a proprietary model with a 1.05M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

OpenAI documents GPT-6.1 Sol as an API model. It supports reasoning effort from low to max; tool calling is supported through the Responses API, while Chat Completions is supported without tool calling.

OpenAI's September 29, 2026 launch charts report exact DeepSWE v1.1, AutomationBench 1.0.6, and Terminal-Bench Science 0.1 results. We use Max effort for the canonical row, even when a lower effort scores higher. The system card adds separate raw and length-adjusted HealthBench results, ExploitGym, ExploitBench, and SEC-Bench Pro. OpenAI also reports 71.4% partial reward on the OSWorld 2.0 offline set, which we keep out of the binary-completion field. GDP.pdf, HealthBench Consensus, MentalHealthBench, and internal safety evaluations remain in the cited sources because their protocols have no confirmed matching field. These are provider-run results; the cyber scores are display-only.

GPT-6.1 Sol sits in the GPT-6 family with GPT-6 Astra, GPT-6 Sol, GPT-6 Luna. Its explicit predecessor is GPT-6 Sol. 11 of 486 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Coding at #8, while its lowest eligible position is Knowledge at #10. particularly well-suited for software development and code generation tasks.

Last updated September 29, 2026. Runtime fields remain blank until a sourced snapshot exists.

Questions

How does GPT-6.1 Sol perform overall in AI benchmarks?

GPT-6.1 Sol ranks #23 out of 210 models on the public BenchAlign leaderboard, with a score of 66.86/100. Its evidence status is Estimated, and this profile shows 11 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is GPT-6.1 Sol good for knowledge and understanding?

GPT-6.1 Sol ranks #10 out of 169 eligible models for knowledge and understanding, with a public category score of 71.3/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GPT-6.1 Sol good for coding and programming?

GPT-6.1 Sol ranks #8 out of 143 eligible models for coding and programming, with a public category score of 67/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GPT-6.1 Sol good for agentic tool use and computer tasks?

GPT-6.1 Sol has source-displayable benchmark coverage for agentic tool use and computer tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Which sibling models are related to GPT-6.1 Sol?

GPT-6.1 Sol belongs to the GPT-6 family. Related tracked variants include GPT-6 Astra, GPT-6 Sol, GPT-6 Luna. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.

Does GPT-6.1 Sol have full benchmark coverage on BenchLM?

No. GPT-6.1 Sol currently has 21 source-displayable rows across 486 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of GPT-6.1 Sol?

GPT-6.1 Sol has a documented context window of 1.05M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.

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