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BenchLM
Anthropic logo
Model A
Claude 4.1 Opus

Anthropic

45.31/100

Supported · Public rank #167

90% interval 42.4–48.3

Claude 4.1 Opus vs Claude Opus 4.8

Updated August 26, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Anthropic logo
Model B
Claude Opus 4.8

Anthropic

76.45/100

Supported · Public rank #8

90% interval 73.8–79.1

Decision reading

Claude Opus 4.8 has the higher public score, 76.45 versus 45.31, and the 90% score intervals do not overlap.

1 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

    Claude Opus 4.8

    Claude Opus 4.8 has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    Claude Opus 4.8

    Claude Opus 4.8 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

  • Repository review cost

    50K fresh input + 3K output tokens

    Claude Opus 4.8

    Claude Opus 4.8 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

  • 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

  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude 4.1 Opus does not fit this workload in one request. Claude 4.1 Opus has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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
1
Claude 4.1 Opus only
1
Claude Opus 4.8 only
32
Like-for-like categories
0 / 8

1 category uses 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
Claude 4.1 Opus
74.5
Claude Opus 4.8
81.1
Weighted basis
1 vs 2 rows
Reading
Directional only

Agentic

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.8
80.3
Weighted basis
0 vs 3 rows
Reading
Not comparable

Reasoning

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.8
72.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.8
62.7
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.8
53.9
Weighted basis
0 vs 3 rows
Reading
Not comparable

Multilingual

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.8
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.8
77.0
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
Claude 4.1 Opus
Not measured
Claude Opus 4.8
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.

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.

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 4.1 Opus
$0.0525
Fits in one request
Claude Opus 4.8
$0.0175
Fits in one request

Claude Opus 4.8 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude 4.1 Opus
$0.975
Fits in one request
Claude Opus 4.8
$0.325
Fits in one request

Claude Opus 4.8 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 4.1 Opus
$4.05
Does not fit in one request
Cached input priced at the published list-input rate
Claude Opus 4.8
$1.35
Fits in one request
Cached input priced at the published list-input rate

Claude 4.1 Opus does not fit this workload in one request. Claude 4.1 Opus has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.8 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 4.1 Opus

200K

Claude Opus 4.8

1M

Anthropic model overview

Cached-input rate

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

Claude 4.1 Opus

Not published

Claude Opus 4.8

Not published

Reasoning profile

Claude 4.1 Opus

Non-Reasoning

Claude Opus 4.8

Reasoning

Weight access

Claude 4.1 Opus

Proprietary

Claude Opus 4.8

Proprietary

License

Claude 4.1 Opus

Proprietary

Claude Opus 4.8

Proprietary

Release date

Claude 4.1 Opus

2025-08-01

Claude Opus 4.8

2026-05-28

If you already use one of these models
Deployment change
Both entries list Anthropic as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
Quality signal
Claude Opus 4.8 has the higher public score, 76.45 versus 45.31, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.975 vs $0.325. Cache-heavy agent loop: $4.05 vs $1.35.
Context tradeoff
Claude Opus 4.8 has the larger documented window (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 evidence34 rows

Agentic

  • JobBench

    Claude 4.1 Opus21.9%
    Source
    Claude Opus 4.8—

    Not directly comparable

  • Terminal-Bench 3.0

    Claude 4.1 Opus—
    Claude Opus 4.821.1%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Claude 4.1 Opus—
    Claude Opus 4.874.6%
    Source

    Not directly comparable

  • BrowseComp

    Claude 4.1 Opus—
    Claude Opus 4.884.3%
    Source

    Not directly comparable

  • DeepSearchQA

    Claude 4.1 Opus—
    Claude Opus 4.893.1%
    Source

    Not directly comparable

  • OSWorld-Verified

    Claude 4.1 Opus—
    Claude Opus 4.883.4%
    Source

    Not directly comparable

  • Finance Agent v2

    Claude 4.1 Opus—
    Claude Opus 4.853.9%
    Source

    Not directly comparable

  • MCP Atlas

    Claude 4.1 Opus—
    Claude Opus 4.882.2%
    Source

    Not directly comparable

  • Toolathlon

    Claude 4.1 Opus—
    Claude Opus 4.859.9%
    Source

    Not directly comparable

  • Gert Labs

    Claude 4.1 Opus—
    Claude Opus 4.872.97%
    Source

    Not directly comparable

  • ResearchClawBench

    Claude 4.1 Opus—
    Claude Opus 4.821.1%
    Source

    Not directly comparable

  • OSWorld 2.0

    Claude 4.1 Opus—
    Claude Opus 4.820.6%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude 4.1 Opus74.5%
    Source
    Claude Opus 4.888.6%
    Source

    Claude Opus 4.8 leads this result

  • SWE-bench Pro

    Claude 4.1 Opus—
    Claude Opus 4.869.2%
    Source

    Not directly comparable

  • SWE Multilingual

    Claude 4.1 Opus—
    Claude Opus 4.884.4%
    Source

    Not directly comparable

  • SWE Multimodal

    Claude 4.1 Opus—
    Claude Opus 4.838.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Claude 4.1 Opus—
    Claude Opus 4.874.6%
    Source

    Not directly comparable

  • cursorBench31

    Claude 4.1 Opus—
    Claude Opus 4.858.4%
    Source

    Not directly comparable

  • cursorBench32

    Claude 4.1 Opus—
    Claude Opus 4.862.3%
    Source

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude 4.1 Opus—
    Claude Opus 4.846.5%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Claude 4.1 Opus—
    Claude Opus 4.872.1%
    Source

    Not directly comparable

  • ARC-AGI-3

    Claude 4.1 Opus—
    Claude Opus 4.81.5%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude 4.1 Opus—
    Claude Opus 4.893.6%
    Source

    Not directly comparable

  • GPQA-D

    Claude 4.1 Opus—
    Claude Opus 4.893.6%
    Source

    Not directly comparable

  • HLE

    Claude 4.1 Opus—
    Claude Opus 4.857.9%
    Source

    Not directly comparable

  • HLE w/o tools

    Claude 4.1 Opus—
    Claude Opus 4.849.8%
    Source

    Not directly comparable

Math

  • USAMO 2026

    Claude 4.1 Opus—
    Claude Opus 4.896.7%
    Source

    Not directly comparable

  • FrontierMath v2 (Tiers 1-3)

    Claude 4.1 Opus—
    Claude Opus 4.847.241%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude 4.1 Opus—
    Claude Opus 4.831.250%
    Source

    Not directly comparable

Multilingual

  • INCLUDE

    Claude 4.1 Opus—
    Claude Opus 4.887.6%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Claude 4.1 Opus—
    Claude Opus 4.866.2%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Claude 4.1 Opus—
    Claude Opus 4.887.9%
    Source

    Not directly comparable

  • CharXiv

    Claude 4.1 Opus—
    Claude Opus 4.889.9%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Claude 4.1 Opus—
    Claude Opus 4.880.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude 4.1 Opus or Claude Opus 4.8?

Claude Opus 4.8 has the higher public score, 76.45 versus 45.31, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Claude 4.1 Opus or Claude Opus 4.8?

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, Claude 4.1 Opus or Claude Opus 4.8?

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 4.1 Opus or Claude Opus 4.8?

For the stated presets, chat costs $0.0525 on Claude 4.1 Opus and $0.0175 on Claude Opus 4.8; repository review costs $0.975 and $0.325; the cache-heavy agent loop costs $4.05 and $1.35. Claude 4.1 Opus does not fit this workload in one request. Claude 4.1 Opus has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude 4.1 Opus or Claude Opus 4.8?

Claude Opus 4.8 has the larger documented context window: 1M, compared with 200K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 26, 2026

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