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BenchLM
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Model A
Claude Sonnet 5

Anthropic

69.84/100

Supported · Public rank #20

90% interval 66.7–73.0

Claude Sonnet 5 vs Fara1.5-27B

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

Microsoft logo
Model B
Fara1.5-27B

Microsoft

—

Evidence status unavailable

90% interval unavailable

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.

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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 Sonnet 5

    Claude Sonnet 5 has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    Fara1.5-27B 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

    Fara1.5-27B is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • 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

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

    Confidence: listed-rates

  • 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

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 Sonnet 5 only
24
Fara1.5-27B only
1
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign 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 Sonnet 5
65.8
Supported · #11/152
Fara1.5-27B
Not ranked
Basis
BenchAlign lane · 6 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
Claude Sonnet 5
64.0
Supported · #13/151
Fara1.5-27B
Not ranked
Basis
BenchAlign lane · 10 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Sonnet 5
77.4
Unranked · 2 rankable rows
Fara1.5-27B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Claude Sonnet 5
66.6
Supported · #20/183
Fara1.5-27B
Not ranked
Basis
BenchAlign lane · 6 vs 0 public rows
Reading
Not comparable

Math

Not comparable
Claude Sonnet 5
Not ranked
Fara1.5-27B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Sonnet 5
Not ranked
Fara1.5-27B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Sonnet 5
77.5
#13/48
Fara1.5-27B
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Sonnet 5
Not ranked
Fara1.5-27B
Not ranked
Basis
Provisional lane · 0 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) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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 Sonnet 5
$0.007
Fits in one request
Fara1.5-27B
Self-hosted; infrastructure cost varies
Fits in one request

Fara1.5-27B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Sonnet 5
$0.13
Fits in one request
Fara1.5-27B
Self-hosted; infrastructure cost varies
Fits in one request

Fara1.5-27B has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Sonnet 5
$0.18
Fits in one request
Fara1.5-27B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Fara1.5-27B has no comparable published API token rate.

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.

Claude Sonnet 5

$0.2 per 1M cached input tokens

Claude API pricing

Fara1.5-27B

No comparable hosted API rate

Microsoft Fara1.5-27B model card

Reasoning profile

Claude Sonnet 5

Reasoning

Fara1.5-27B

Reasoning

Weight access

Claude Sonnet 5

Proprietary

Fara1.5-27B

Open Weight

License

Claude Sonnet 5

Proprietary

Fara1.5-27B

Open Weight

Release date

Claude Sonnet 5

2026-06-30

Fara1.5-27B

2026-07-17

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Claude Sonnet 5 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 evidence25 rows

Agentic

  • Terminal-Bench 3.0

    Claude Sonnet 514.6%
    Source
    Fara1.5-27B—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Sonnet 580.4%
    Source
    Fara1.5-27B—

    Not directly comparable

  • BrowseComp

    Claude Sonnet 584.7%
    Source
    Fara1.5-27B—

    Not directly comparable

  • HLE w/ tools

    Claude Sonnet 557.4%
    Source
    Fara1.5-27B—

    Not directly comparable

  • OSWorld-Verified

    Claude Sonnet 581.2%
    Source
    Fara1.5-27B—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Sonnet 574.5%
    Source
    Fara1.5-27B—

    Not directly comparable

  • WebVoyager

    Claude Sonnet 5—
    Fara1.5-27B89.3%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Sonnet 585.2%
    Source
    Fara1.5-27B—

    Not directly comparable

  • SWE-bench Pro

    Claude Sonnet 563.2%
    Source
    Fara1.5-27B—

    Not directly comparable

  • SWE Multilingual

    Claude Sonnet 578.3%
    Source
    Fara1.5-27B—

    Not directly comparable

  • SWE Multimodal

    Claude Sonnet 528.1%
    Source
    Fara1.5-27B—

    Not directly comparable

  • Terminal-Bench 2.0

    Claude Sonnet 580.4%
    Source
    Fara1.5-27B—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Sonnet 542.7%
    Source
    Fara1.5-27B—

    Not directly comparable

  • cursorBench32

    Claude Sonnet 561.5%
    Source
    Fara1.5-27B—

    Not directly comparable

  • VulcanBench CII v1

    Claude Sonnet 589.2%
    Source
    Fara1.5-27B—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Sonnet 582.4%
    Source
    Fara1.5-27B—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Sonnet 579.6%
    Source
    Fara1.5-27B—

    Not directly comparable

Knowledge

  • HLE

    Claude Sonnet 557.4%
    Source
    Fara1.5-27B—

    Not directly comparable

  • HLE w/o tools

    Claude Sonnet 543.2%
    Source
    Fara1.5-27B—

    Not directly comparable

  • HLE-Verified

    Claude Sonnet 531.0%
    Source
    Fara1.5-27B—

    Not directly comparable

  • LABBench2

    Claude Sonnet 580.1%
    Source
    Fara1.5-27B—

    Not directly comparable

  • GPQA Diamond (Vals)

    Claude Sonnet 588.9%
    Source
    Fara1.5-27B—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Sonnet 587.5%
    Source
    Fara1.5-27B—

    Not directly comparable

Multimodal

  • CharXiv

    Claude Sonnet 588.3%
    Source
    Fara1.5-27B—

    Not directly comparable

  • CharXiv w/o tools

    Claude Sonnet 577%
    Source
    Fara1.5-27B—

    Not directly comparable

Frequently asked questions

Which is better, Claude Sonnet 5 or Fara1.5-27B?

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 Sonnet 5 or Fara1.5-27B?

Fara1.5-27B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude Sonnet 5 or Fara1.5-27B?

Fara1.5-27B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Sonnet 5 or Fara1.5-27B?

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 Sonnet 5 or Fara1.5-27B?

Claude Sonnet 5 has the larger documented context window: 1M, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 10, 2026

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