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Data

GPT-6 Luna vs Interfaze Beta

Updated November 6, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.

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Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
OpenAI logo
GPT-6 Luna

OpenAI

62.59/100

Supported · Public rank #43

90% interval 52.0–73.2

Model B
Interfaze logo
Interfaze Beta

Interfaze

—

Evidence status unavailable

90% interval unavailable

Shared results
0
GPT-6 Luna only
23
Interfaze Beta only
9
Like-for-like categories
0 / 8
Supported: GPT-6 LunaHow 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.

  • Long documents

    Prompts that approach the documented context limit

    GPT-6 Luna

    GPT-6 Luna has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    GPT-6 Luna

    GPT-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-6 Luna

    GPT-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-6 Luna

    GPT-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

    Interfaze Beta is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Interfaze Beta is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

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.

51.8GPT-6 Luna—Interfaze Beta

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
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.

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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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.

Agentic

Not comparable
GPT-6 Luna
53.5
Estimated · #45/131
Interfaze Beta
Not ranked
Basis
BenchAlign v5.8 lane · 5 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
GPT-6 Luna
51.8
Supported · #42/157
Interfaze Beta
Not ranked
Basis
BenchAlign v5.8 lane · 3 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
GPT-6 Luna
57.5
#22/25
Interfaze Beta
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
GPT-6 Luna
81.9
Unranked · 1 rankable row
Interfaze Beta
61.0
Unranked · 5 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Knowledge

Not comparable
GPT-6 Luna
63.3
Supported · #40/178
Interfaze Beta
Not ranked
Basis
BenchAlign v5.8 lane · 5 vs 3 public rows
Reading
Not comparable

Multilingual

Not comparable
GPT-6 Luna
Not ranked
Interfaze Beta
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
GPT-6 Luna
Not ranked
Interfaze Beta
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
GPT-6 Luna
80.2
Unranked · 2 rankable rows
Interfaze Beta
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
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-6 Luna
$0.00035
Fits in one request
Interfaze Beta
$0.00325
Fits in one request

GPT-6 Luna has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

GPT-6 Luna
$0.0065
Fits in one request
Interfaze Beta
$0.0855
Fits in one request

GPT-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-6 Luna
$0.009
Fits in one request
Interfaze Beta
$0.365
Fits in one request
Cached input priced at the published list-input rate

GPT-6 Luna has the lower modeled cost

Interfaze Beta has no published cached-input rate, so cached tokens use its listed input 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.

Cached-input rate

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

GPT-6 Luna

$0.01 per 1M cached input tokens

OpenAI GPT-6 Luna model documentation

Interfaze Beta

Not published

Provider availability

GPT-6 Luna

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

OpenAI GPT-6 Sol and Luna launch

Interfaze Beta

Not sourced

Reasoning profile

GPT-6 Luna

Reasoning

Interfaze Beta

Reasoning

Weight access

GPT-6 Luna

Proprietary

Interfaze Beta

Proprietary

License

GPT-6 Luna

Proprietary

Interfaze Beta

Proprietary

Release date

GPT-6 Luna

2026-09-22

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
Repository review: $0.0065 vs $0.0855. Cache-heavy agent loop: $0.009 vs $0.365.
Context tradeoff
GPT-6 Luna has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GPT-6 Luna or Interfaze Beta?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-6 Luna or Interfaze Beta?

Interfaze Beta is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, GPT-6 Luna or Interfaze Beta?

Interfaze Beta is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, GPT-6 Luna or Interfaze Beta?

For the stated presets, chat costs $0.00035 on GPT-6 Luna and $0.00325 on Interfaze Beta; repository review costs $0.0065 and $0.0855; the cache-heavy agent loop costs $0.009 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-6 Luna or Interfaze Beta?

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

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

  • ExploitGym

    GPT-6 Luna11.6%
    Source
    Interfaze Beta—

    Not directly comparable

  • Hermes Index

    GPT-6 Luna33.9%
    Source
    Interfaze Beta—

    Not directly comparable

  • Hermes Bench

    GPT-6 Luna70.5%
    Source
    Interfaze Beta—

    Not directly comparable

  • MazeBench

    GPT-6 Luna0%
    Source
    Interfaze Beta—

    Not directly comparable

  • OSWorld 2.1 Offline

    GPT-6 Luna48.9%
    Source
    Interfaze Beta—

    Not directly comparable

Coding

  • DeepSWE

    GPT-6 Luna66.6%
    Source
    Interfaze Beta—

    Not directly comparable

  • Bug Hunt Bench

    GPT-6 Luna18.3 fixes
    Source
    Interfaze Beta—

    Not directly comparable

  • FrontierCode 2.0

    GPT-6 Luna21.9%
    Source
    Interfaze Beta—

    Not directly comparable

  • Spider 2.0-Lite

    GPT-6 Luna—
    Interfaze Beta52.9%
    Source

    Not directly comparable

Reasoning

  • BioBasicsBench

    GPT-6 Luna37.6%
    Source
    Interfaze Beta—

    Not directly comparable

  • ARC-AGI-1

    GPT-6 Luna86.67%
    Source
    Interfaze Beta—

    Not directly comparable

  • ARC-AGI-2

    GPT-6 Luna59.3%
    Source
    Interfaze Beta—

    Not directly comparable

  • ARC-AGI-3

    GPT-6 Luna0.1%
    Source
    Interfaze Beta—

    Not directly comparable

  • ARC-AGI-3 Provider Adapter

    GPT-6 Luna0.59%
    Source
    Interfaze Beta—

    Not directly comparable

Multimodal

  • ExtractBench Pro

    GPT-6 Luna0%
    Source
    Interfaze Beta—

    Not directly comparable

  • ExtractBench (LlamaIndex)

    GPT-6 Luna91.01%
    Source
    Interfaze Beta—

    Not directly comparable

  • ParseBench

    GPT-6 Luna64.13%
    Source
    Interfaze Beta—

    Not directly comparable

  • OCRBench V2

    GPT-6 Luna—
    Interfaze Beta70.7%
    Source

    Not directly comparable

  • olmOCR

    GPT-6 Luna—
    Interfaze Beta85.7%
    Source

    Not directly comparable

  • RefCOCO (avg)

    GPT-6 Luna—
    Interfaze Beta82.1%
    Source

    Not directly comparable

  • MMMU-Pro

    GPT-6 Luna—
    Interfaze Beta71.1%
    Source

    Not directly comparable

Knowledge

  • HealthBench (raw)

    GPT-6 Luna50.0%
    Source
    Interfaze Beta—

    Not directly comparable

  • HealthBench (length-adjusted)

    GPT-6 Luna54.5%
    Source
    Interfaze Beta—

    Not directly comparable

  • HealthBench Professional

    GPT-6 Luna60.8%
    Source
    Interfaze Beta—

    Not directly comparable

  • HealthBench Professional (raw)

    GPT-6 Luna61.2%
    Source
    Interfaze Beta—

    Not directly comparable

  • HealthBench Hard

    GPT-6 Luna31.4%
    Source
    Interfaze Beta—

    Not directly comparable

  • GPQA

    GPT-6 Luna—
    Interfaze Beta89.9%
    Source

    Not directly comparable

  • GPQA-D

    GPT-6 Luna—
    Interfaze Beta89.9%
    Source

    Not directly comparable

  • MMMLU

    GPT-6 Luna—
    Interfaze Beta90.9%
    Source

    Not directly comparable

Instruction following

  • SOB Value Acc

    GPT-6 Luna—
    Interfaze Beta79.5%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    GPT-6 Luna78.947%
    Source
    Interfaze Beta—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    GPT-6 Luna56.098%
    Source
    Interfaze Beta—

    Not directly comparable

32 public results · 0 shared

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Last updated November 6, 2026