Long documents
Prompts that approach the documented context limit
Muse Spark 1.1
Muse Spark 1.1 has the larger documented context window.
Confidence: documented
GPT-5.1-Codex vs Muse Spark 1.1
Updated October 7, 2026. Rank says Muse Spark 1.1 is ahead. Price, access, and your workload can each overturn that. 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
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Decision reading
Muse Spark 1.1 has the higher public point estimate, 65.95 versus 50.16. Their conditional score ranges overlap. These ranges do not establish rank confidence. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- GPT-5.1-Codex only
- 2
- Muse Spark 1.1 only
- 24
- Like-for-like categories
- 0 / 8
Estimated: GPT-5.1-Codex · Supported: Muse Spark 1.1. Conditional ranges do not establish rank confidence.How 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-5.1-Codex is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.1-Codex is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-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-ratesRepository 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.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
35.1GPT-5.1-Codex54.2Muse Spark 1.1
Directional only · BenchAlign v5.8
Muse Spark 1.1 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.
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.
2 GPT-5.1-Codex only
2 Shared
24 Muse Spark 1.1 only
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
LiveCodeBench (Vals)Coding
Normalized gap 0.3GPT-5.1-Codex:85.6%Muse Spark 1.1:85.9%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.
Coding
Directional only- GPT-5.1-Codex
- 35.1
- Estimated · #79/146
- Muse Spark 1.1
- 54.2
- Supported · #35/146
- Basis
- BenchAlign v5.8 lane · 2 vs 4 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.1-Codex
- 50.5
- Estimated · #65/174
- Muse Spark 1.1
- 66.7
- Supported · #21/174
- Basis
- BenchAlign v5.8 lane · 0 vs 5 public rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.1-Codex
- Not ranked
- Muse Spark 1.1
- 56.9
- Supported · #32/122
- Basis
- BenchAlign v5.8 lane · 2 vs 14 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.1-Codex
- 69.9
- Unranked · 2 rankable rows
- Muse Spark 1.1
- 75.7
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.1-Codex
- 67.9
- Unranked · 1 rankable row
- Muse Spark 1.1
- 78.3
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.1-Codex
- Not ranked
- Muse Spark 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.1-Codex
- 84.2
- #42/125
- Muse Spark 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.1-Codex
- Not ranked
- Muse Spark 1.1
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.1-Codex | Muse Spark 1.1 | Basis | Reading |
|---|---|---|---|---|
| Coding | 35.1Estimated · #79/146 | 54.2Supported · #35/146 | Directional onlyBenchAlign v5.8 lane · 2 vs 4 public rows | Directional only |
| Knowledge | 50.5Estimated · #65/174 | 66.7Supported · #21/174 | Directional onlyBenchAlign v5.8 lane · 0 vs 5 public rows | Directional only |
| Agentic | Not ranked | 56.9Supported · #32/122 | Not comparableBenchAlign v5.8 lane · 2 vs 14 public rows | Not comparable |
| Reasoning | 69.9Unranked · 2 rankable rows | 75.7Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 67.9Unranked · 1 rankable row | 78.3Unranked · 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 | 84.2#42/125 | 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 v5.8) 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.
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-5.1-Codex
- $0.00625
- 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-5.1-Codex
- $0.0925
- 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-5.1-Codex
- $0.15
- 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.
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-5.1-CodexMuse Spark 1.1
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.1-Codex
Muse Spark 1.1
1M
API model ID
GPT-5.1-Codex
gpt-5.1-codex
OpenAI GPT-5.1-Codex model documentationMuse 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-5.1-Codex
$0.125 per 1M cached input tokens
OpenAI GPT-5.1-Codex model documentationMuse Spark 1.1
No comparable hosted API rate
Documented inputs
GPT-5.1-Codex
Not sourced
Muse Spark 1.1
Not sourced
Documented outputs
GPT-5.1-Codex
Not sourced
Muse Spark 1.1
Not sourced
Provider availability
GPT-5.1-Codex
Not sourced
Muse Spark 1.1
Not sourced
Reasoning profile
GPT-5.1-Codex
Reasoning
Muse Spark 1.1
Reasoning
Weight access
GPT-5.1-Codex
Proprietary
Muse Spark 1.1
Proprietary
License
GPT-5.1-Codex
Proprietary
Muse Spark 1.1
Proprietary
Release date
GPT-5.1-Codex
2025-10-15
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
- Muse Spark 1.1 has the higher public point estimate, 65.95 versus 50.16. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Muse Spark 1.1 has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.1-Codex or Muse Spark 1.1?
Muse Spark 1.1 has the higher public point estimate, 65.95 versus 50.16. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.1-Codex or Muse Spark 1.1?
Muse Spark 1.1 scores higher for coding on the public lane, 54.2 to 35.1. GPT-5.1-Codex 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-5.1-Codex or Muse Spark 1.1?
GPT-5.1-Codex is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.1-Codex 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-5.1-Codex or Muse Spark 1.1?
Muse Spark 1.1 has the larger documented context window: 1M, compared with 400K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence28 rows
Agentic
Gert Labs
GPT-5.1-Codex49.68%SourceMuse Spark 1.1—Not directly comparable
JobBench
GPT-5.1-Codex26.2%SourceMuse Spark 1.154.7%SourceMuse Spark 1.1 leads this result
Terminal-Bench 2.1
GPT-5.1-Codex—Muse Spark 1.180.0%SourceNot directly comparable
MCP Atlas
GPT-5.1-Codex—Muse Spark 1.188.1%SourceNot directly comparable
Toolathlon
GPT-5.1-Codex—Muse Spark 1.175.6%SourceNot directly comparable
OSWorld-Verified
GPT-5.1-Codex—Muse Spark 1.180.8%SourceNot directly comparable
WebArena-Verified
GPT-5.1-Codex—Muse Spark 1.169%SourceNot directly comparable
DeepSearchQA
GPT-5.1-Codex—Muse Spark 1.184.9%SourceNot directly comparable
CyberGym
GPT-5.1-Codex—Muse Spark 1.159.0%SourceNot directly comparable
Finance Agent v2
GPT-5.1-Codex—Muse Spark 1.157.2%SourceNot directly comparable
deepSwe
GPT-5.1-Codex—Muse Spark 1.153.3%SourceNot directly comparable
OSWorld 2.0
GPT-5.1-Codex—Muse Spark 1.114.2%SourceNot directly comparable
Cybench
GPT-5.1-Codex—Muse Spark 1.192.9%SourceNot directly comparable
ExploitGym
GPT-5.1-Codex—Muse Spark 1.10.8%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.1-Codex—Muse Spark 1.169.3%SourceNot directly comparable
Coding
Vibe Code Bench
GPT-5.1-Codex13.12%SourceMuse Spark 1.1—Not directly comparable
LiveCodeBench (Vals)
GPT-5.1-Codex85.6%SourceMuse Spark 1.185.9%SourceMuse Spark 1.1 leads this result
Terminal-Bench 2.1
GPT-5.1-Codex—Muse Spark 1.180.0%SourceNot directly comparable
SWE-bench Pro
GPT-5.1-Codex—Muse Spark 1.161.5%SourceNot directly comparable
SWE-bench (Vals)
GPT-5.1-Codex—Muse Spark 1.182.0%SourceNot directly comparable
Reasoning
MRCR 1M
GPT-5.1-Codex—Muse Spark 1.154.1%SourceNot directly comparable
Multimodal
Knowledge
HLE
GPT-5.1-Codex—Muse Spark 1.162.1%SourceNot directly comparable
HLE w/o tools
GPT-5.1-Codex—Muse Spark 1.152.2%SourceNot directly comparable
HealthBench Professional
GPT-5.1-Codex—Muse Spark 1.159.3%SourceNot directly comparable
GPQA Diamond (Vals)
GPT-5.1-Codex—Muse Spark 1.191.2%SourceNot directly comparable
MMLU-Pro (Vals)
GPT-5.1-Codex—Muse Spark 1.188.7%SourceNot directly comparable
28 public results · 2 shared
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Last updated October 7, 2026