GPT-6 Sol vs Muse Spark 1.1

Decision reading

GPT-6 Sol has the higher public score estimate, 80.45 versus 69.91, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-6 Sol

OpenAI

80.45/100

Estimated · Public rank #7

90% interval 51.1–92.0

Model B

Muse Spark 1.1

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.

Long documents
Prompts that approach the documented context limit
GPT-6 Sol
GPT-6 Sol has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-6 Sol is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-6 Sol is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
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.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

74.3GPT-6 Sol59.9Muse Spark 1.1

Directional only · BenchAlign

GPT-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.

Open the coding leaderboard Shortlist models for this job

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

7 GPT-6 Sol only

3 Shared

23 Muse Spark 1.1 only

Shared results: 3
GPT-6 Sol only: 7
Muse Spark 1.1 only: 23
Like-for-like categories: 0 / 8

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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

Directional only

GPT-6 Sol: 69.7; Estimated · #7/157
Muse Spark 1.1: 59.3; Supported · #29/157
Basis: BenchAlign lane · 4 vs 14 public rows
Reading: Directional only

Coding

Directional only

GPT-6 Sol: 74.3; Estimated · #6/159
Muse Spark 1.1: 59.9; Supported · #27/159
Basis: BenchAlign lane · 1 vs 4 public rows
Reading: Directional only

Knowledge

Directional only

GPT-6 Sol: 80.2; Estimated · #6/189
Muse Spark 1.1: 70.5; Supported · #13/189
Basis: BenchAlign lane · 5 vs 5 public rows
Reading: Directional only

Reasoning

Not comparable

GPT-6 Sol: 78.5; #5/17
Muse Spark 1.1: 74.4; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-6 Sol: 82.8; Unranked · 1 rankable row
Muse Spark 1.1: 77.3; Unranked · 2 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-6 Sol: Not ranked
Muse Spark 1.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Instruction following

Not comparable

GPT-6 Sol: Not ranked
Muse Spark 1.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-6 Sol: Not ranked
Muse Spark 1.1: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-6 Sol and Muse Spark 1.1
Category	GPT-6 Sol	Muse Spark 1.1	Basis	Reading
Agentic	69.7Estimated · #7/157	59.3Supported · #29/157	Directional onlyBenchAlign lane · 4 vs 14 public rows	Directional only
Coding	74.3Estimated · #6/159	59.9Supported · #27/159	Directional onlyBenchAlign lane · 1 vs 4 public rows	Directional only
Knowledge	80.2Estimated · #6/189	70.5Supported · #13/189	Directional onlyBenchAlign lane · 5 vs 5 public rows	Directional only
Reasoning	78.5#5/17	74.4Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multimodal	82.8Unranked · 1 rankable row	77.3Unranked · 2 rankable rows	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
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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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.

OSWorld 2.0
Agentic
GPT-6 Sol: 60.5%Muse Spark 1.1: 14.2%Normalized gap 46.3GPT-6 Sol source Muse Spark 1.1 source

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 Sol: $0.007; Fits in one request
Muse Spark 1.1: API rate not published; Fits in one request

Muse Spark 1.1 has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-6 Sol: $0.13; Fits in one request
Muse Spark 1.1: API rate not published; Fits in one request

Muse Spark 1.1 has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-6 Sol: $0.18; Fits in one request
Muse Spark 1.1: API rate not published; Fits in one request; Cached-input rate unavailable

Muse Spark 1.1 has no comparable published API token rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGPT-6 SolMuse Spark 1.1

Context window

Maximum documented context; output-token limits may be lower.

GPT-6 Sol

1.05M

OpenAI GPT-6 Sol model documentation

Muse Spark 1.1

API model ID

GPT-6 Sol

gpt-6-sol

OpenAI GPT-6 Sol model documentation

Muse Spark 1.1

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GPT-6 Sol

$0.2 per 1M cached input tokens

OpenAI GPT-6 Sol model documentation

Muse Spark 1.1

No comparable hosted API rate

Documented inputs

GPT-6 Sol

text, image

OpenAI GPT-6 Sol model documentation

Muse Spark 1.1

Not sourced

Documented outputs

GPT-6 Sol

text

OpenAI GPT-6 Sol model documentation

Muse Spark 1.1

Not sourced

Provider availability

GPT-6 Sol

Generally Available · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Work, Codex

OpenAI GPT-6 Sol and Luna launch

Muse Spark 1.1

Not sourced

Reasoning profile

GPT-6 Sol

Reasoning

Muse Spark 1.1

Reasoning

Weight access

GPT-6 Sol

Proprietary

Muse Spark 1.1

Proprietary

License

GPT-6 Sol

Proprietary

Muse Spark 1.1

Proprietary

Release date

GPT-6 Sol

2026-09-16

Muse Spark 1.1

2026-07-09

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: GPT-6 Sol has the higher public score estimate, 80.45 versus 69.91, but the 90% score intervals overlap.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: GPT-6 Sol has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence33 rows

Agentic

Agents' Last Exam
GPT-6 Sol56.4%
Source
Muse Spark 1.1—
Not directly comparable
AutomationBench
GPT-6 Sol33.2%
Source
Muse Spark 1.1—
Not directly comparable
OSWorld 2.0
GPT-6 Sol60.5%
Source
Muse Spark 1.114.2%
Source
GPT-6 Sol leads this result
ExploitGym
GPT-6 Sol22.1%
Source
Muse Spark 1.10.8%
Source
GPT-6 Sol leads this result
Terminal-Bench 2.0
GPT-6 Sol—
Muse Spark 1.180%
Source
Not directly comparable
MCP Atlas
GPT-6 Sol—
Muse Spark 1.188.1%
Source
Not directly comparable
Toolathlon
GPT-6 Sol—
Muse Spark 1.175.6%
Source
Not directly comparable
OSWorld-Verified
GPT-6 Sol—
Muse Spark 1.180.8%
Source
Not directly comparable
WebArena-Verified
GPT-6 Sol—
Muse Spark 1.169%
Source
Not directly comparable
DeepSearchQA
GPT-6 Sol—
Muse Spark 1.184.9%
Source
Not directly comparable
CyberGym
GPT-6 Sol—
Muse Spark 1.159.0%
Source
Not directly comparable
Finance Agent v2
GPT-6 Sol—
Muse Spark 1.157.2%
Source
Not directly comparable
deepSwe
GPT-6 Sol—
Muse Spark 1.153.3%
Source
Not directly comparable
JobBench
GPT-6 Sol—
Muse Spark 1.154.7%
Source
Not directly comparable
Cybench
GPT-6 Sol—
Muse Spark 1.192.9%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-6 Sol—
Muse Spark 1.169.3%
Source
Not directly comparable

Coding

DeepSWE
GPT-6 Sol68.8%
Source
Muse Spark 1.1—
Not directly comparable
Terminal-Bench 2.0
GPT-6 Sol—
Muse Spark 1.180.0%
Source
Not directly comparable
SWE-bench Pro
GPT-6 Sol—
Muse Spark 1.161.5%
Source
Not directly comparable
LiveCodeBench (Vals)
GPT-6 Sol—
Muse Spark 1.185.9%
Source
Not directly comparable
SWE-bench (Vals)
GPT-6 Sol—
Muse Spark 1.182.0%
Source
Not directly comparable

Reasoning

MRCR 1M
GPT-6 Sol—
Muse Spark 1.154.1%
Source
Not directly comparable

Multimodal

CharXiv
GPT-6 Sol—
Muse Spark 1.188.4%
Source
Not directly comparable
BabyVision
GPT-6 Sol—
Muse Spark 1.176.3%
Source
Not directly comparable

Knowledge

HealthBench (raw)
GPT-6 Sol47.1%
Source
Muse Spark 1.1—
Not directly comparable
HealthBench (length-adjusted)
GPT-6 Sol53.2%
Source
Muse Spark 1.1—
Not directly comparable
HealthBench Professional
GPT-6 Sol60.8%
Source
Muse Spark 1.159.3%
Source
GPT-6 Sol leads this result
HealthBench Professional (raw)
GPT-6 Sol59.5%
Source
Muse Spark 1.1—
Not directly comparable
HealthBench Hard
GPT-6 Sol30.1%
Source
Muse Spark 1.1—
Not directly comparable
HLE
GPT-6 Sol—
Muse Spark 1.162.1%
Source
Not directly comparable
HLE w/o tools
GPT-6 Sol—
Muse Spark 1.152.2%
Source
Not directly comparable
GPQA Diamond (Vals)
GPT-6 Sol—
Muse Spark 1.191.2%
Source
Not directly comparable
MMLU-Pro (Vals)
GPT-6 Sol—
Muse Spark 1.188.7%
Source
Not directly comparable

Questions

Which is better, GPT-6 Sol or Muse Spark 1.1?

GPT-6 Sol has the higher public score estimate, 80.45 versus 69.91, 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 Sol or Muse Spark 1.1?

GPT-6 Sol scores higher for coding on the public lane, 74.3 to 59.9. GPT-6 Sol 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-6 Sol or Muse Spark 1.1?

GPT-6 Sol scores higher for agentic tasks on the public lane, 69.7 to 59.3. GPT-6 Sol is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-6 Sol or Muse Spark 1.1?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-6 Sol or Muse Spark 1.1?

GPT-6 Sol has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 22, 2026

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GPT-6 Sol vs Muse Spark 1.1

Which one for your work

Long documents

Coding work

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Knowledge

Reasoning

Multimodal

Multilingual

Instruction following

Math

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Reasoning

Multimodal

Knowledge

Questions

Which is better, GPT-6 Sol or Muse Spark 1.1?

Which is better for coding, GPT-6 Sol or Muse Spark 1.1?

Which is better for agentic tasks, GPT-6 Sol or Muse Spark 1.1?

Which costs less, GPT-6 Sol or Muse Spark 1.1?

Which has the larger context window, GPT-6 Sol or Muse Spark 1.1?

Related comparisons

Watch GPT-6 Sol vs Muse Spark 1.1