Knowledge
Like-for-like- GPT-5.6 Luna
- 92.3
- Interfaze Beta
- 89.9
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-5.6 Luna leads
Model comparison
GPT-5.6 Luna vs Interfaze Beta
Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
3 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.
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
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. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
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
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
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.
19 GPT-5.6 Luna only
3 Shared
6 Interfaze Beta only
- Shared results
- 3
- GPT-5.6 Luna only
- 19
- Interfaze Beta only
- 6
- Like-for-like categories
- 2 / 8
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.
Multimodal
Like-for-like- GPT-5.6 Luna
- 78.4
- Interfaze Beta
- 71.1
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-5.6 Luna leads
Agentic
Not comparable- GPT-5.6 Luna
- 84.1
- Interfaze Beta
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.6 Luna
- 62.7
- Interfaze Beta
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.6 Luna
- 59.5
- Interfaze Beta
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.6 Luna
- 73.6
- Interfaze Beta
- Not measured
- Weighted basis
- 2 vs 0 rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.6 Luna
- Not measured
- Interfaze Beta
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.6 Luna
- Not measured
- Interfaze Beta
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | GPT-5.6 Luna | Interfaze Beta | Weighted basis | Reading |
|---|---|---|---|---|
| Knowledge | 92.3 | 89.9 | Like-for-like1 vs 1 rows | GPT-5.6 Luna leads |
| Multimodal | 78.4 | 71.1 | Like-for-like1 vs 1 rows | GPT-5.6 Luna leads |
| Agentic | 84.1 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Coding | 62.7 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Reasoning | 59.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Math | 73.6 | Not measured | Not comparable2 vs 0 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 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.
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
- $0.0008
- Fits in one request
- Interfaze Beta
- $0.00325
- 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
- $0.0136
- Fits in one request
- Interfaze Beta
- $0.0855
- 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
- $0.02
- Fits in one request
- Interfaze Beta
- $0.365
- Fits in one request
- Cached input priced at the published list-input rate
GPT-5.6 Luna has the lower modeled cost
Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.6 LunaInterfaze Beta
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.6 Luna
1.05M
OpenAI model catalogInterfaze Beta
1M
API model ID
GPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogInterfaze Beta
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
$0.02 per 1M cached input tokens
OpenAI pricingInterfaze Beta
Not published
Documented inputs
GPT-5.6 Luna
text, image
OpenAI model catalogInterfaze Beta
Not sourced
Documented outputs
GPT-5.6 Luna
Interfaze Beta
Not sourced
Provider availability
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogInterfaze Beta
Not sourced
Reasoning profile
GPT-5.6 Luna
Reasoning
Interfaze Beta
Reasoning
Weight access
GPT-5.6 Luna
Proprietary
Interfaze Beta
Proprietary
License
GPT-5.6 Luna
Proprietary
Interfaze Beta
Proprietary
Release date
GPT-5.6 Luna
2026-07-09
Interfaze Beta
2026-05-11
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.0136 vs $0.0855. Cache-heavy agent loop: $0.02 vs $0.365.
- 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 evidence28 rows
Agentic
Terminal-Bench 2.0
GPT-5.6 Luna84.7%SourceInterfaze Beta—Not directly comparable
BrowseComp
GPT-5.6 Luna83.3%SourceInterfaze Beta—Not directly comparable
OSWorld 2.0
GPT-5.6 Luna45.6%SourceInterfaze Beta—Not directly comparable
CyberGym
GPT-5.6 Luna77.9%SourceInterfaze Beta—Not directly comparable
ExploitGym
GPT-5.6 Luna12.4%SourceInterfaze Beta—Not directly comparable
Toolathlon
GPT-5.6 Luna53.4%SourceInterfaze Beta—Not directly comparable
Coding
SWE-bench Pro
GPT-5.6 Luna62.7%SourceInterfaze Beta—Not directly comparable
Terminal-Bench 2.0
GPT-5.6 Luna84.7%SourceInterfaze Beta—Not directly comparable
deepSwe
GPT-5.6 Luna67.2%SourceInterfaze Beta—Not directly comparable
FrontierCode 1.1 Extended
GPT-5.6 Luna55.1%SourceInterfaze Beta—Not directly comparable
cursorBench32
GPT-5.6 Luna61.1%SourceInterfaze Beta—Not directly comparable
Spider 2.0-Lite
GPT-5.6 Luna—Interfaze Beta52.9%SourceNot directly comparable
Reasoning
Knowledge
GPQA
GPT-5.6 Luna92.3%SourceInterfaze Beta89.9%SourceGPT-5.6 Luna leads this result
GPQA-D
GPT-5.6 Luna92.3%SourceInterfaze Beta89.9%SourceGPT-5.6 Luna leads this result
HealthBench Professional
GPT-5.6 Luna55.7%SourceInterfaze Beta—Not directly comparable
HealthBench Hard
GPT-5.6 Luna32.0%SourceInterfaze Beta—Not directly comparable
MMMLU
GPT-5.6 Luna—Interfaze Beta90.9%SourceNot directly comparable
Math
Multimodal
MMMU-Pro
GPT-5.6 Luna78.4%SourceInterfaze Beta71.1%SourceGPT-5.6 Luna leads this result
MMMU-Pro w/ Python
GPT-5.6 Luna79.5%SourceInterfaze Beta—Not directly comparable
OCRBench V2
GPT-5.6 Luna—Interfaze Beta70.7%SourceNot directly comparable
olmOCR
GPT-5.6 Luna—Interfaze Beta85.7%SourceNot directly comparable
RefCOCO (avg)
GPT-5.6 Luna—Interfaze Beta82.1%SourceNot directly comparable
Instruction following
SOB Value Acc
GPT-5.6 Luna—Interfaze Beta79.5%SourceNot directly comparable
Frequently asked questions
Which is better, GPT-5.6 Luna or Interfaze Beta?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.6 Luna or Interfaze Beta?
The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.
Which is better for agentic tasks, GPT-5.6 Luna or Interfaze Beta?
The published evidence does not provide a shared weighted agentic tasks basis for both models, so BenchLM does not name a agentic tasks winner.
Which costs less, GPT-5.6 Luna or Interfaze Beta?
For the stated presets, chat costs $0.0008 on GPT-5.6 Luna and $0.00325 on Interfaze Beta; repository review costs $0.0136 and $0.0855; the cache-heavy agent loop costs $0.02 and $0.365. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.6 Luna or Interfaze Beta?
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 1M.
Related comparisons
Last updated August 10, 2026
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