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BenchLM

Claude 3.5 Sonnet vs Claude Opus 4.5 Thinking

Updated September 29, 2026. Rank says Claude Opus 4.5 Thinking is ahead. Price, access, and your workload can each overturn that. 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
Anthropic logo

Anthropic

30/100

Estimated · Public rank #174

90% interval 21.5–38.5

Model B
Anthropic logo

Anthropic

54.66/100

Estimated · Public rank #63

90% interval 43.1–66.2

Shared results
0
Claude 3.5 Sonnet only
4
Claude Opus 4.5 Thinking only
1
Like-for-like categories
0 / 8
Estimated: Claude 3.5 Sonnet and Claude Opus 4.5 ThinkingHow 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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

Show secondary and unsupported calls
  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Claude 3.5 Sonnet and Claude Opus 4.5 Thinking are 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

    Claude 3.5 Sonnet and Claude Opus 4.5 Thinking are not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented
  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates
  • 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 3.5 Sonnet does not fit this workload in one request. Claude Opus 4.5 Thinking does not fit this workload in one request. Claude 3.5 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.5 Thinking has no comparable published API token rate.

    Confidence: rate-fallback
  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

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.

—Claude 3.5 Sonnet—Claude Opus 4.5 Thinking

Not comparable · BenchAlign v5.7

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.

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 v5.7 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
Claude 3.5 Sonnet
Not ranked
Claude Opus 4.5 Thinking
Not ranked
Basis
BenchAlign v5.7 lane · 0 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
Claude 3.5 Sonnet
Not ranked
Claude Opus 4.5 Thinking
Not ranked
Basis
BenchAlign v5.7 lane · 1 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude 3.5 Sonnet
Not ranked
Claude Opus 4.5 Thinking
75.5
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude 3.5 Sonnet
Not ranked
Claude Opus 4.5 Thinking
70.1
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Claude 3.5 Sonnet
Not ranked
Claude Opus 4.5 Thinking
53.6
Estimated · #51/169
Basis
BenchAlign v5.7 lane · 1 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Claude 3.5 Sonnet
Not ranked
Claude Opus 4.5 Thinking
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude 3.5 Sonnet
Not ranked
Claude Opus 4.5 Thinking
68.5
#66/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude 3.5 Sonnet
25.6
Unranked · 2 rankable rows
Claude Opus 4.5 Thinking
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 v5.7) 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.

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

Claude 3.5 Sonnet
$0.0105
Fits in one request
Claude Opus 4.5 Thinking
API rate not published
Fits in one request

Claude Opus 4.5 Thinking has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude 3.5 Sonnet
$0.195
Fits in one request
Claude Opus 4.5 Thinking
API rate not published
Fits in one request

Claude Opus 4.5 Thinking has no comparable published API token rate.

Cache-heavy agent loop

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

Claude 3.5 Sonnet
$0.81
Does not fit in one request
Cached input priced at the published list-input rate
Claude Opus 4.5 Thinking
API rate not published
Does not fit in one request
Cached-input rate unavailable

Claude 3.5 Sonnet does not fit this workload in one request. Claude Opus 4.5 Thinking does not fit this workload in one request. Claude 3.5 Sonnet has no published cached-input rate, so cached tokens use its listed input rate. Claude Opus 4.5 Thinking has no comparable published API token 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.

Context window

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

Claude 3.5 Sonnet

200K

Claude Opus 4.5 Thinking

200K

API model ID

Claude 3.5 Sonnet

Not sourced

Claude Opus 4.5 Thinking

Not sourced

Cached-input rate

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

Claude 3.5 Sonnet

Not published

Claude Opus 4.5 Thinking

No comparable hosted API rate

Documented inputs

Claude 3.5 Sonnet

Not sourced

Claude Opus 4.5 Thinking

Not sourced

Documented outputs

Claude 3.5 Sonnet

Not sourced

Claude Opus 4.5 Thinking

Not sourced

Provider availability

Claude 3.5 Sonnet

Not sourced

Claude Opus 4.5 Thinking

Not sourced

Reasoning profile

Claude 3.5 Sonnet

Non-Reasoning

Claude Opus 4.5 Thinking

Reasoning

Weight access

Claude 3.5 Sonnet

Proprietary

Claude Opus 4.5 Thinking

Proprietary

License

Claude 3.5 Sonnet

Proprietary

Claude Opus 4.5 Thinking

Proprietary

Release date

Claude 3.5 Sonnet

2024-06-01

Claude Opus 4.5 Thinking

2025-11-01

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Both models list 200K.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude 3.5 Sonnet or Claude Opus 4.5 Thinking?

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 3.5 Sonnet or Claude Opus 4.5 Thinking?

Claude 3.5 Sonnet and Claude Opus 4.5 Thinking are not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude 3.5 Sonnet or Claude Opus 4.5 Thinking?

Claude 3.5 Sonnet and Claude Opus 4.5 Thinking are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude 3.5 Sonnet or Claude Opus 4.5 Thinking?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Claude 3.5 Sonnet or Claude Opus 4.5 Thinking?

Both models list the same context window, 200K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence5 rows

Coding

  • SWE-bench Verified

    Claude 3.5 Sonnet49%
    Source
    Claude Opus 4.5 Thinking—

    Not directly comparable

  • Vibe Code Bench

    Claude 3.5 Sonnet—
    Claude Opus 4.5 Thinking20.63%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Claude 3.5 Sonnet59.4%
    Source
    Claude Opus 4.5 Thinking—

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Claude 3.5 Sonnet2.069%
    Source
    Claude Opus 4.5 Thinking—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Claude 3.5 Sonnet0.000%
    Source
    Claude Opus 4.5 Thinking—

    Not directly comparable

5 public results · 0 shared

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Last updated September 29, 2026