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Model comparison

Claude Opus 4.7 vs Interfaze Beta

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Claude Opus 4.7

Anthropic

71.2/100

Supported · Public rank #15

90% interval 59.5–82.8

Interfaze Beta

Interfaze

Evidence status unavailable

90% interval unavailable

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 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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    Interfaze Beta

    Interfaze Beta 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

    Interfaze Beta

    Interfaze Beta has the lower estimated token cost for this stated workload. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. 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

    Interfaze Beta

    Interfaze Beta 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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
0
Claude Opus 4.7 only
8
Interfaze Beta only
9
Like-for-like categories
0 / 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.

Agentic

Not comparable
Claude Opus 4.7
Not measured
Interfaze Beta
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Claude Opus 4.7
Not measured
Interfaze Beta
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Claude Opus 4.7
Not measured
Interfaze Beta
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Claude Opus 4.7
Not measured
Interfaze Beta
89.9
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
Claude Opus 4.7
38.6
Interfaze Beta
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 4.7
Not measured
Interfaze Beta
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude Opus 4.7
Not measured
Interfaze Beta
71.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 4.7
Not measured
Interfaze Beta
Not measured
Weighted basis
0 vs 0 rows
Reading
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.

A shared-evidence shape is not available.

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

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

Claude Opus 4.7
$0.0175
Fits in one request
Interfaze Beta
$0.00325
Fits in one request

Interfaze Beta has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Opus 4.7
$0.325
Fits in one request
Interfaze Beta
$0.0855
Fits in one request

Interfaze Beta has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Claude Opus 4.7
$1.35
Fits in one request
Cached input priced at the published list-input rate
Interfaze Beta
$0.365
Fits in one request
Cached input priced at the published list-input rate

Interfaze Beta has the lower modeled cost

Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. 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.

Context window

Maximum documented context; output-token limits may be lower.

Claude Opus 4.7

Interfaze Beta

1M

Cached-input rate

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

Claude Opus 4.7

Not published

Interfaze Beta

Not published

Reasoning profile

Claude Opus 4.7

Non-Reasoning

Interfaze Beta

Reasoning

Weight access

Claude Opus 4.7

Proprietary

Interfaze Beta

Proprietary

License

Claude Opus 4.7

Proprietary

Interfaze Beta

Proprietary

Release date

Claude Opus 4.7

2026-04-16

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: $0.325 vs $0.0855. Cache-heavy agent loop: $1.35 vs $0.365.
Context tradeoff
Both models list 1M.

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 evidence17 rows

Agentic

  • Gert Labs

    Claude Opus 4.765.59%
    Source
    Interfaze Beta

    Not directly comparable

  • ResearchClawBench

    Claude Opus 4.720.7%
    Source
    Interfaze Beta

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 4.713.9%
    Source
    Interfaze Beta

    Not directly comparable

Coding

  • Vibe Code Bench

    Claude Opus 4.771.00%
    Source
    Interfaze Beta

    Not directly comparable

  • React Native Evals

    Claude Opus 4.782.8%
    Source
    Interfaze Beta

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Opus 4.738.5%
    Source
    Interfaze Beta

    Not directly comparable

  • Spider 2.0-Lite

    Claude Opus 4.7
    Interfaze Beta52.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude Opus 4.7
    Interfaze Beta89.9%
    Source

    Not directly comparable

  • GPQA-D

    Claude Opus 4.7
    Interfaze Beta89.9%
    Source

    Not directly comparable

  • MMMLU

    Claude Opus 4.7
    Interfaze Beta90.9%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Claude Opus 4.743.793%
    Source
    Interfaze Beta

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude Opus 4.722.917%
    Source
    Interfaze Beta

    Not directly comparable

Multimodal

  • OCRBench V2

    Claude Opus 4.7
    Interfaze Beta70.7%
    Source

    Not directly comparable

  • olmOCR

    Claude Opus 4.7
    Interfaze Beta85.7%
    Source

    Not directly comparable

  • RefCOCO (avg)

    Claude Opus 4.7
    Interfaze Beta82.1%
    Source

    Not directly comparable

  • MMMU-Pro

    Claude Opus 4.7
    Interfaze Beta71.1%
    Source

    Not directly comparable

Instruction following

  • SOB Value Acc

    Claude Opus 4.7
    Interfaze Beta79.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 4.7 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, Claude Opus 4.7 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, Claude Opus 4.7 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, Claude Opus 4.7 or Interfaze Beta?

For the stated presets, chat costs $0.0175 on Claude Opus 4.7 and $0.00325 on Interfaze Beta; repository review costs $0.325 and $0.0855; the cache-heavy agent loop costs $1.35 and $0.365. Claude Opus 4.7 has no published cached-input rate, so cached tokens use its listed input rate. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Opus 4.7 or Interfaze Beta?

Both models list the same context window, 1M.

Related comparisons

Last updated August 10, 2026

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