Updated November 13, 2026. Rank says GPT-6.1 Sol is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GPT-6.1 Sol has the higher public score, 84.63 versus 65.11, and the 90% score intervals do not overlap. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
GPT-6.1 Sol: $2 input / $10 output
Muse Spark 1.2: $1.25 input / $4.25 output
GPT-6.1 Sol: 1.05M
Muse Spark 1.2: 1M
GPT-6.1 Sol: September 29, 2026
Muse Spark 1.2: August 5, 2026
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.
Code generation, repair, and software-engineering tasks
GPT-6.1 Sol
GPT-6.1 Sol has the higher public coding point estimate, 73.4 to 50.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
GPT-6.1 Sol
GPT-6.1 Sol has the higher public agentic point estimate, 65.7 to 57.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-6.1 Sol
GPT-6.1 Sol has the larger documented context window.
1K fresh input + 500 output tokens
Muse Spark 1.2
Muse Spark 1.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Muse Spark 1.2
Muse Spark 1.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Muse Spark 1.2
Muse Spark 1.2 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.
Like-for-like · BenchAlign v5.8
GPT-6.1 Sol has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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.
| Category | GPT-6.1 Sol | Muse Spark 1.2 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 65.7Supported · #17/135 | 57.7Supported · #33/135 | Like-for-likeBenchAlign v5.8 lane · 7 vs 3 public rows | GPT-6.1 Sol leads · intervals overlap |
| Coding | 73.4Supported · #8/162 | 50.1Supported · #47/162 | Like-for-likeBenchAlign v5.8 lane · 6 vs 5 public rows | GPT-6.1 Sol leads |
| Knowledge | 85.6Supported · #3/180 | 63.1Supported · #33/180 | Like-for-likeBenchAlign v5.8 lane · 5 vs 1 public rows | GPT-6.1 Sol leads |
| Reasoning | 80.4Supported · #5/160 | 60.3Estimated · #31/160 | Directional onlyBenchAlign v5.8 lane · 6 vs 0 public rows | Directional only |
| Multimodal | 78.9Supported · #4/67 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 0 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 | 93.8Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) 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.
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.
1K fresh input + 500 output tokens
Muse Spark 1.2 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Muse Spark 1.2 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Muse Spark 1.2 has the lower modeled cost
Costs use the listed standard API rates.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
GPT-6.1 Sol
Muse Spark 1.2
GPT-6.1 Sol
gpt-6.1-sol
OpenAI GPT-6.1 Sol model documentationMuse Spark 1.2
muse-spark-1.2
Meta: Muse Spark 1.2 model pageA 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 documentationMuse Spark 1.2
$0.15 per 1M cached input tokens
Meta: Muse Spark 1.2 model pageGPT-6.1 Sol
text, image
OpenAI GPT-6.1 Sol model documentationMuse Spark 1.2
Not sourced
GPT-6.1 Sol
Muse Spark 1.2
Not sourced
GPT-6.1 Sol
Generally Available · OpenAI Responses API, OpenAI Chat Completions API
OpenAI GPT-6.1 Sol model documentationMuse Spark 1.2
Not sourced
GPT-6.1 Sol
Reasoning
Muse Spark 1.2
Reasoning
GPT-6.1 Sol
Proprietary
Muse Spark 1.2
Proprietary
GPT-6.1 Sol
Proprietary
Muse Spark 1.2
Proprietary
GPT-6.1 Sol
2026-09-29
Muse Spark 1.2
2026-08-05
GPT-6.1 Sol has the higher public score, 84.63 versus 65.11, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
GPT-6.1 Sol has the higher public coding point estimate, 73.4 to 50.1, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
GPT-6.1 Sol has the higher public agentic tasks point estimate, 65.7 to 57.7, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.007 on GPT-6.1 Sol and $0.00337 on Muse Spark 1.2; repository review costs $0.13 and $0.07525; the cache-heavy agent loop costs $0.16 and $0.0975. Costs use the listed standard API rates.
GPT-6.1 Sol has the larger documented context window: 1.05M, compared with 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 4.1
Not directly comparable
AutomationBench
Not directly comparable
Terminal-Bench-Science 0.1
Not directly comparable
ExploitGym
Not directly comparable
MazeBench
GPT-6.1 Sol leads this result
Hermes Index
Not directly comparable
Hermes Bench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
ScarfBench
Not directly comparable
DeepSWE
GPT-6.1 Sol leads this result
Bug Hunt Bench
Not directly comparable
FrontierCode 2.0
Not directly comparable
VulcanBench Frontier v4
Not directly comparable
PostTrainBench
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
VulcanBench v3
Not directly comparable
FrontierSWE v2
Not directly comparable
SWE-bench (Vals)
Not directly comparable
BioBasicsBench
Not directly comparable
STEMBench
Not directly comparable
ARC-AGI-1
Not directly comparable
ARC-AGI-2
Not directly comparable
ARC-AGI-3
Not directly comparable
ARC-AGI-3 Provider Adapter
Not directly comparable
HealthBench (raw)
Not directly comparable
HealthBench (length-adjusted)
Not directly comparable
HealthBench Professional
Not directly comparable
HealthBench Professional (raw)
Not directly comparable
HealthBench Hard
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
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Last updated November 13, 2026
