GPT-6.1 Sol API model profile

Evidence snapshot:

GPT-6.1 Sol is a OpenAI model profile with a 1,050,000-token context window. The dated catalog snapshot records file, image, text modalities and interfaces for agentic, coding, file input, prompt caching, reasoning.

GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series. It is suited for agentic coding, computer use, document-heavy professional work, and multi-step business workflow automation, and approaches Astra-level results on these tasks at a much lower cost. Compared with GPT-6 Sol, it makes fewer factual errors and is more reliable at respecting explicit restrictions and user intent during agentic tasks.

GPT-6.1 Sol is worth reaching for when you need a route scoring in the top quarter on the published intelligence index and carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support.

GPT-6.1 Sol is available under the Kendr alias kc-gpt-6.1-sol. Current account availability and customer credit quotes come from Kendr's live public model API and applicable account policy.

GPT-6.1 Sol accepts file, image, and text and returns text. The snapshot records agentic, coding, file input, prompt caching, reasoning, reasoning controls, structured output, text generation, tool calling, tools, vision, web search, and web search pricing as capabilities or interfaces.

The dated reference snapshot lists $1 input and $5 output per million tokens. Cached input is $0.05 per million tokens. The separately published provider-route reference is $2 / $10 (Standard global inference, ≤272K input; $0.10/M cache read; live route quote applies).

The 2026-10-13 snapshot includes 1 Artificial Analysis index, 6 Design Arena categories, and AutoExacto results for 6 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.

GPT-6.1 Sol ranked #16 in the cited trailing-7-day third-party catalog usage dataset as of 2026-10-13. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

Profile data was reviewed for the 2026-10-13 snapshot. Claims retain their source dates, and unavailable fields are shown as unavailable instead of estimated.

Profile facts

Profile type
Kendr API model
Kendr API alias
kc-gpt-6.1-sol
Model developer
OpenAI
Kendr route provider
OpenAI
Context window
1M
Snapshot date
2026-10-13
Knowledge cutoff
Not disclosed
Reference catalog ID
openai/gpt-6.1-sol

When to pick GPT-6.1 Sol

GPT-6.1 Sol is worth reaching for when you need a route scoring in the top quarter on the published intelligence index and carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support.

Each condition below is derived from this model's own values in the 2026-10-13 snapshot, compared only against models publishing the same field.

  • Reach for it when a whole corpus has to fit in one prompt. The 1.05M-token context window is in the top quarter of the 167 models publishing a limit here, so long documents can go in whole rather than chunked.
  • Reach for it when answer quality matters more than unit cost. It scores 51.8 on the Artificial Analysis intelligence index, above three quarters of the 119 models carrying that field in the 2026-10-13 snapshot.
  • Reach for it when the input includes images. The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described.
  • Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
  • Look elsewhere when the request is trivial and latency-sensitive. Reasoning is mandatory on this route and cannot be turned off, so short factual requests still pay reasoning tokens and reasoning latency.

Context, modalities, and identity

GPT-6.1 Sol accepts file, image, and text and returns text. The snapshot records agentic, coding, file input, prompt caching, reasoning, reasoning controls, structured output, text generation, tool calling, tools, vision, web search, and web search pricing as capabilities or interfaces.

Model developer
OpenAI
Kendr route provider
OpenAI
Context window
1M
Maximum output
128,000 tokens
Input modalities
file, image, and text
Output modalities
text
Knowledge cutoff
Not disclosed

Model overview

GPT-6.1 Sol is an upgrade to GPT-6 Sol from OpenAI, positioned below the flagship GPT-6 Astra in the GPT-6 series. It is suited for agentic coding, computer use, document-heavy professional work, and multi-step business workflow automation, and approaches Astra-level results on these tasks at a much lower cost. Compared with GPT-6 Sol, it makes fewer factual errors and is more reliable at respecting explicit restrictions and user intent during agentic tasks.

Reference model ID
openai/gpt-6.1-sol
Canonical version
openai/gpt-6.1-sol-20260929
Hugging Face ID
Not separately documented

Capabilities and supported controls

Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.

Supported parameters
include_reasoning, max_completion_tokens, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, tool_choice, tools, verbosity
  • agentic
  • coding
  • file input
  • prompt caching
  • reasoning
  • reasoning controls
  • structured output
  • text generation
  • tool calling
  • tools
  • vision
  • web search
  • web search pricing

Dated pricing snapshot

The dated reference snapshot lists $1 input and $5 output per million tokens. Cached input is $0.05 per million tokens. The separately published provider-route reference is $2 / $10 (Standard global inference, ≤272K input; $0.10/M cache read; live route quote applies).

Reference figures are dated 2026-10-13; the live Kendr quote can differ by selected provider route, context tier, caching, tools, region, and current rate card.

Price date
2026-10-13
Input
$1 per 1M tokens
Cached input
$0.05 per 1M tokens
Output
$5 per 1M tokens
Prompt thresholdInputCached inputOutput
From 272,000 prompt tokens$4 / 1M$0.2 / 1M$15 / 1M

Provider routes, performance, and uptime

The snapshot retains 8 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.

Active provider endpoints
8
Best p50 latency
1.84 s
Best p50 throughput
83 tok/s
Availability with routing
95.16% over the sampled window
Availability without routing
92.34% over the sampled window
Performance date
2026-10-13
ProviderQuantizationInput / 1MOutput / 1MCache read / 1MContextp50 latencyp50 throughputUptime (1d)
OpenAIunknown$1 / 1M$5 / 1M$0.05 / 1M1,050,000 tokens10.38 s30 tok/s96.03%
Azureunknown$2 / 1M$10 / 1M$0.1 / 1M1,050,000 tokens11.57 s33 tok/s100.00%
OpenAIunknown$2 / 1M$10 / 1M$0.1 / 1M1,050,000 tokens7.32 s38 tok/s100.00%
Amazon Bedrockunknown$2.2 / 1M$11 / 1M$0.11 / 1M1,050,000 tokens6.30 s44 tok/s100.00%
Azureunknown$2.2 / 1M$11 / 1M$0.11 / 1M1,050,000 tokens5.91 s36 tok/s100.00%
Azureunknown$2.2 / 1M$11 / 1M$0.11 / 1M1,050,000 tokens4.68 s28 tok/s100.00%
OpenAIunknown$4 / 1M$20 / 1M$0.2 / 1M1,050,000 tokens3.06 s65 tok/s100.00%
OpenAIunknown$12 / 1M$60 / 1M$0.6 / 1M1,050,000 tokens1.84 s83 tok/s100.00%

Benchmark evidence and limitations

The 2026-10-13 snapshot includes 1 Artificial Analysis index, 6 Design Arena categories, and AutoExacto results for 6 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.

Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

Intelligence Index
51.8
AutoExacto coverage
6 provider observations over 32 days
Design Arena categoryEloWin rateRank
3d147673.5%#3
codecategories134358.2%#5
dataviz132257.4%#10
gamedev137962.2%#4
uicomponent135259.7%#4
website131955.7%#6

AutoExacto provider benchmarks

Rolling provider observations from the cited third-party benchmark view. The lookback is 32 days; GPQA Diamond and TAU-Bench Airline measure different abilities and should not be blended into one score.

ProviderGPQA DiamondTAU-Bench AirlineRuns
Amazon Bedrock94.78%72.67%1
auto-routing94.44%70%1
Azure93.34%68.34%2
Azure93.94%67.7%2
Azure94.2%68.98%2
OpenAI94.44%68.25%3

Popularity and market context

GPT-6.1 Sol ranked #16 in the cited trailing-7-day third-party catalog usage dataset as of 2026-10-13. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

Third-party catalog rank
#16
Global market share
Not inferred
Observation date
2026-10-13

OpenAI-compatible API example

This example applies to the published Kendr alias on this hosted profile. Check the live public catalog before use.

curl https://api.kendr.org/v1/chat/completions \
  -H "Authorization: Bearer $KENDR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"kc-gpt-6.1-sol","messages":[{"role":"user","content":"Hello"}]}'

Frequently asked questions

When should I use GPT-6.1 Sol?

GPT-6.1 Sol is worth reaching for when you need a route scoring in the top quarter on the published intelligence index and carrying one of the widest context windows in the catalog. The conditions below are the ones its own numbers support. Reach for it when a whole corpus has to fit in one prompt: The 1.05M-token context window is in the top quarter of the 167 models publishing a limit here, so long documents can go in whole rather than chunked. Reach for it when answer quality matters more than unit cost: It scores 51.8 on the Artificial Analysis intelligence index, above three quarters of the 119 models carrying that field in the 2026-10-13 snapshot. Reach for it when the input includes images: The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described. Reach for it when the model has to call tools: Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose. Look elsewhere when the request is trivial and latency-sensitive: Reasoning is mandatory on this route and cannot be turned off, so short factual requests still pay reasoning tokens and reasoning latency.

What is GPT-6.1 Sol?

GPT-6.1 Sol is a OpenAI model profile with a 1,050,000-token context window. The dated catalog snapshot records file, image, text modalities and interfaces for agentic, coding, file input, prompt caching, reasoning.

What context window does GPT-6.1 Sol have?

The dated profile lists 1M of context and up to 128,000 output tokens. Provider routes, variants, and runtime configuration can impose lower effective limits.

What benchmark evidence is available for GPT-6.1 Sol?

The 2026-10-13 snapshot includes 1 Artificial Analysis index, 6 Design Arena categories, and AutoExacto results for 6 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable. Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

What is the Kendr API alias for GPT-6.1 Sol?

Use kc-gpt-6.1-sol as the model value. The live public model API and signed-in catalog remain authoritative for route availability and customer credit quotes.

How is GPT-6.1 Sol priced on Kendr?

The dated reference snapshot lists $1 input and $5 output per million tokens. Cached input is $0.05 per million tokens. The separately published provider-route reference is $2 / $10 (Standard global inference, ≤272K input; $0.10/M cache read; live route quote applies). Kendr applies one 5% markup to configured provider model cost; the live quote and settled routing receipt are authoritative for a request.

Sources and evidence dates

  1. GPT-6.1 Sol third-party catalog record (catalog, checked 2026-10-13)
  2. Third-party model catalog methodology (methodology, checked 2026-10-13)
  3. OpenAI official model documentation (primary, checked 2026-10-13)
  4. Artificial Analysis capability indices methodology (benchmark, checked 2026-10-13)
  5. Design Arena leaderboard and methodology (benchmark, checked 2026-10-13)
  6. GPT-6.1 Sol third-party catalog record (benchmark, checked 2026-10-13)
  7. GPT-6.1 Sol provider or route reference (primary, checked 2026-10-13)