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BenchLM
Model A
Agents-A1-F16-GGUF

InternScience

—

Evidence status unavailable

90% interval unavailable

Agents-A1-F16-GGUF vs Claude Mythos 5

Updated August 21, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
Claude Mythos 5

Anthropic

83.0/100

Supported · Public rank #1

90% interval 79.6–86.3

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 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 Mythos 5

    Claude Mythos 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

    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

  • 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
Agents-A1-F16-GGUF only
0
Claude Mythos 5 only
15
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
Agents-A1-F16-GGUF
Not measured
Claude Mythos 5
87.0
Weighted basis
0 vs 3 rows
Reading
Not comparable

Coding

Not comparable
Agents-A1-F16-GGUF
Not measured
Claude Mythos 5
89.7
Weighted basis
0 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
Agents-A1-F16-GGUF
Not measured
Claude Mythos 5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Agents-A1-F16-GGUF
Not measured
Claude Mythos 5
68.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Agents-A1-F16-GGUF
Not measured
Claude Mythos 5
97.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Agents-A1-F16-GGUF
Not measured
Claude Mythos 5
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Agents-A1-F16-GGUF
Not measured
Claude Mythos 5
93.5
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
Agents-A1-F16-GGUF
Not measured
Claude Mythos 5
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

Agents-A1-F16-GGUF
API rate not published
Fits in one request
Claude Mythos 5
$0.035
Fits in one request

Agents-A1-F16-GGUF has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Agents-A1-F16-GGUF
API rate not published
Fits in one request
Claude Mythos 5
$0.65
Fits in one request

Agents-A1-F16-GGUF has no comparable published API token rate.

Cache-heavy agent loop

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

Agents-A1-F16-GGUF
API rate not published
Fits in one request
Cached-input rate unavailable
Claude Mythos 5
$0.9
Fits in one request

Agents-A1-F16-GGUF 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.

Context window

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

Agents-A1-F16-GGUF

262K

Claude Mythos 5

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.

Agents-A1-F16-GGUF

No comparable hosted API rate

Claude Mythos 5

$1 per 1M cached input tokens

Claude API pricing

Reasoning profile

Agents-A1-F16-GGUF

Reasoning

Claude Mythos 5

Reasoning

Weight access

Agents-A1-F16-GGUF

Open Weight

Claude Mythos 5

Proprietary

License

Agents-A1-F16-GGUF

Open Weight

Claude Mythos 5

Proprietary

Release date

Agents-A1-F16-GGUF

2026-07-02

Claude Mythos 5

2026-06-09

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 Mythos 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 evidence15 rows

Agentic

  • Terminal-Bench 2.0

    Agents-A1-F16-GGUF—
    Claude Mythos 588%
    Source

    Not directly comparable

  • OSWorld-Verified

    Agents-A1-F16-GGUF—
    Claude Mythos 585%
    Source

    Not directly comparable

  • BrowseComp

    Agents-A1-F16-GGUF—
    Claude Mythos 588%
    Source

    Not directly comparable

  • CyberGym

    Agents-A1-F16-GGUF—
    Claude Mythos 583.8%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Agents-A1-F16-GGUF—
    Claude Mythos 595.5%
    Source

    Not directly comparable

  • SWE-bench Pro

    Agents-A1-F16-GGUF—
    Claude Mythos 580.3%
    Source

    Not directly comparable

  • Terminal-Bench 2.0

    Agents-A1-F16-GGUF—
    Claude Mythos 588.0%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Agents-A1-F16-GGUF—
    Claude Mythos 594.1%
    Source

    Not directly comparable

  • HLE

    Agents-A1-F16-GGUF—
    Claude Mythos 564.5%
    Source

    Not directly comparable

  • HLE w/o tools

    Agents-A1-F16-GGUF—
    Claude Mythos 559%
    Source

    Not directly comparable

Math

  • USAMO 2026

    Agents-A1-F16-GGUF—
    Claude Mythos 597.6%
    Source

    Not directly comparable

Multilingual

  • SWE Multilingual

    Agents-A1-F16-GGUF—
    Claude Mythos 592.2%
    Source

    Not directly comparable

Multimodal

  • SWE-bench Multimodal

    Agents-A1-F16-GGUF—
    Claude Mythos 554.9%
    Source

    Not directly comparable

  • CharXiv

    Agents-A1-F16-GGUF—
    Claude Mythos 593.5%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Agents-A1-F16-GGUF—
    Claude Mythos 588.9%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Agents-A1-F16-GGUF or Claude Mythos 5?

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, Agents-A1-F16-GGUF or Claude Mythos 5?

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, Agents-A1-F16-GGUF or Claude Mythos 5?

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, Agents-A1-F16-GGUF or Claude Mythos 5?

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, Agents-A1-F16-GGUF or Claude Mythos 5?

Claude Mythos 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 August 21, 2026

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