Agentic
Like-for-like- GPT-5.6 Luna
- 84.1
- Inkling
- 69.4
- Weighted basis
- 2 vs 2 rows
- Reading
- GPT-5.6 Luna leads
GPT-5.6 Luna vs Inkling
Updated August 29, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.6 Luna has the higher public score estimate, 67.35 versus 67.02, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
7 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.6 Luna
GPT-5.6 Luna leads on the same 2 weighted benchmark rows.
Confidence: limited
Long documents
Prompts that approach the documented context limit
GPT-5.6 Luna
GPT-5.6 Luna has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
17 GPT-5.6 Luna only
7 Shared
8 Inkling only
- Shared results
- 7
- GPT-5.6 Luna only
- 17
- Inkling only
- 8
- Like-for-like categories
- 1 / 8
3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Coding
Directional only- GPT-5.6 Luna
- 62.7
- Inkling
- 68.6
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.6 Luna
- 92.3
- Inkling
- 51.6
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Multimodal
Directional only- GPT-5.6 Luna
- 78.4
- Inkling
- 76.5
- Weighted basis
- 1 vs 2 rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.6 Luna
- 59.5
- Inkling
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Luna
- 73.6
- Inkling
- 97.1
- Weighted basis
- 2 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Luna
- Not measured
- Inkling
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Luna
- Not measured
- Inkling
- 79.8
- Weighted basis
- 0 vs 1 rows
- Reading
- Not comparable
| Category | GPT-5.6 Luna | Inkling | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 84.1 | 69.4 | Like-for-like2 vs 2 rows | GPT-5.6 Luna leads |
| Coding | 62.7 | 68.6 | Directional only1 vs 2 rows | Directional only |
| Knowledge | 92.3 | 51.6 | Directional only1 vs 2 rows | Directional only |
| Multimodal | 78.4 | 76.5 | Directional only1 vs 2 rows | Directional only |
| Reasoning | 59.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 73.6 | 97.1 | Not comparable2 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | 79.8 | Not comparable0 vs 1 rows | Not comparable |
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.
Terminal-Bench 2.0
Agentic
SWE-bench Pro
Coding
BrowseComp
Agentic
MMMU-Pro
Multimodal
GPQA
Knowledge
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-5.6 Luna
- $0.004
- Fits in one request
- Inkling
- $0.00421
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.6 Luna
- $0.068
- Fits in one request
- Inkling
- $0.10754
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.6 Luna
- $0.1
- Fits in one request
- Inkling
- $0.159
- Fits in one request
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.6 LunaInkling
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Luna
1.05M
OpenAI model catalogInkling
1M
API model ID
GPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogInkling
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Luna
$0.1 per 1M cached input tokens
OpenAI API pricingInkling
$0.374 per 1M cached input tokens
Documented inputs
GPT-5.6 Luna
text, image
OpenAI model catalogInkling
Not sourced
Documented outputs
GPT-5.6 Luna
Inkling
Not sourced
Provider availability
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogInkling
Not sourced
Reasoning profile
GPT-5.6 Luna
Reasoning
Inkling
Hybrid
Weight access
GPT-5.6 Luna
Proprietary
Inkling
Open Weight
License
GPT-5.6 Luna
Proprietary
Inkling
Open Weight
Release date
GPT-5.6 Luna
2026-07-09
Inkling
2026-07-15
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-5.6 Luna has the higher public score estimate, 67.35 versus 67.02, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.068 vs $0.10754. Cache-heavy agent loop: $0.1 vs $0.159.
- Context tradeoff
- GPT-5.6 Luna 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 evidence32 rows
Agentic
Terminal-Bench 3.0
GPT-5.6 Luna14.3%SourceInkling—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna84.7%SourceInkling63.8%SourceGPT-5.6 Luna leads this result
BrowseComp
GPT-5.6 Luna83.3%SourceInkling77.1%SourceGPT-5.6 Luna leads this result
OSWorld 2.0
GPT-5.6 Luna45.6%SourceInkling—Not directly comparable
CyberGym
GPT-5.6 Luna77.9%SourceInkling—Not directly comparable
ExploitGym
GPT-5.6 Luna12.4%SourceInkling—Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%SourceInkling—Not directly comparable
MCP Atlas
GPT-5.6 Luna—Inkling74.1%SourceNot directly comparable
Coding
SWE-bench Pro
GPT-5.6 Luna62.7%SourceInkling54.3%SourceGPT-5.6 Luna leads this result
Terminal-Bench 2.0
GPT-5.6 Luna84.7%SourceInkling63.8%SourceGPT-5.6 Luna leads this result
deepSwe
GPT-5.6 Luna67.2%SourceInkling—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%SourceInkling—Not directly comparable
cursorBench32
GPT-5.6 Luna61.1%SourceInkling—Not directly comparable
VulcanBench v3
GPT-5.6 Luna85.5%SourceInkling—Not directly comparable
SWE-bench Verified
GPT-5.6 Luna—Inkling77.6%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.6 Luna92.3%SourceInkling87.9%SourceGPT-5.6 Luna leads this result
GPQA-D
GPT-5.6 Luna92.3%SourceInkling87.9%SourceGPT-5.6 Luna leads this result
HealthBench Professional
GPT-5.6 Luna55.7%SourceInkling—Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%SourceInkling—Not directly comparable
HLE
GPT-5.6 Luna—Inkling46%SourceNot directly comparable
HLE w/o tools
GPT-5.6 Luna—Inkling30%SourceNot directly comparable
Math
FrontierMath (legacy)
GPT-5.6 Luna78.6%SourceInkling—Not directly comparable
FrontierMath v2 (Tiers 1-3)
GPT-5.6 Luna78.600%SourceInkling—Not directly comparable
FrontierMath v2 (Tier 4)
GPT-5.6 Luna58.500%SourceInkling—Not directly comparable
AIME26
GPT-5.6 Luna—Inkling97.1%SourceNot directly comparable
Multimodal
Instruction following
IFBench
GPT-5.6 Luna—Inkling79.8%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.6 Luna or Inkling?
GPT-5.6 Luna has the higher public score estimate, 67.35 versus 67.02, 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-5.6 Luna or Inkling?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, GPT-5.6 Luna or Inkling?
GPT-5.6 Luna leads the like-for-like agentic tasks comparison across 2 shared weighted benchmark rows.
Which costs less, GPT-5.6 Luna or Inkling?
For the stated presets, chat costs $0.004 on GPT-5.6 Luna and $0.00421 on Inkling; repository review costs $0.068 and $0.10754; the cache-heavy agent loop costs $0.1 and $0.159. Costs use the listed standard API rates.
Which has the larger context window, GPT-5.6 Luna or Inkling?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated August 29, 2026
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