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BenchLM
Data

Claude Mythos 5.2 vs Quasar 438B

Updated November 10, 2026. We do not rank this pair: at least one has no public score. 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
Claude Mythos 5.2

Anthropic

—

Evidence status unavailable

90% interval unavailable

Model B
Quasar 438B

Multiverse Computing

—

Evidence status unavailable

90% interval unavailable

API price per 1M tokens

Claude Mythos 5.2: $8 input / $40 output

Quasar 438B: $0.60 input / $1.80 output

Context window

Claude Mythos 5.2: 1M

Quasar 438B: 1M

Released

Claude Mythos 5.2: November 9, 2026

Quasar 438B: September 2, 2026

Shared results
0
Claude Mythos 5.2 only
14
Quasar 438B only
2
Like-for-like categories
0 / 8
How 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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    Quasar 438B

    Quasar 438B 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

    Quasar 438B

    Quasar 438B has the lower estimated token cost for this stated workload. Quasar 438B 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

    Quasar 438B

    Quasar 438B 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

    Claude Mythos 5.2 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

    Claude Mythos 5.2 is 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

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 Mythos 5.240.6Quasar 438B

Not comparable · BenchAlign v5.8

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.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

Or describe your own job

The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.

Describe the task and constraints. This selector recommends models; it cannot carry out the task or access your files and accounts.

We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy

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 v5.8 lane for agentic, coding, reasoning, multimodal, 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 Mythos 5.2
Not ranked
Quasar 438B
39.9
Estimated · #73/132
Basis
BenchAlign v5.8 lane · 5 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
Claude Mythos 5.2
Not ranked
Quasar 438B
40.6
Estimated · #70/158
Basis
BenchAlign v5.8 lane · 0 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Claude Mythos 5.2
Not ranked
Quasar 438B
Not ranked
Basis
BenchAlign v5.8 lane · 0 vs 0 public rows
Reading
Not comparable

Multimodal

Not comparable
Claude Mythos 5.2
Not ranked
Quasar 438B
Not ranked
Basis
BenchAlign v5.8 lane · 1 vs 0 public rows
Reading
Not comparable

Knowledge

Not comparable
Claude Mythos 5.2
Not ranked
Quasar 438B
Not ranked
Basis
BenchAlign v5.8 lane · 8 vs 0 public rows
Reading
Not comparable

Multilingual

Not comparable
Claude Mythos 5.2
Not ranked
Quasar 438B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Mythos 5.2
Not ranked
Quasar 438B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Mythos 5.2
Not ranked
Quasar 438B
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, reasoning, multimodal, and knowledge denominators (BenchAlign v5.8) 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 Mythos 5.2
$0.028
Fits in one request
Quasar 438B
$0.0015
Fits in one request

Quasar 438B has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Mythos 5.2
$0.52
Fits in one request
Quasar 438B
$0.0354
Fits in one request

Quasar 438B 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 Mythos 5.2
$0.6
Fits in one request
Quasar 438B
$0.15
Fits in one request
Cached input priced at the published list-input rate

Quasar 438B has the lower modeled cost

Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.

Your workload

Enter a month of tokens to price it at both models’ listed API rates.

Claude Mythos 5.2
$160.00
per month
Quasar 438B
$9.60
per month

Standard listed rates, no batch or volume discounts.

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.

Reasoning profile

Claude Mythos 5.2

Reasoning

Quasar 438B

Reasoning

Weight access

Claude Mythos 5.2

Proprietary

Quasar 438B

Proprietary

License

Claude Mythos 5.2

Proprietary

Quasar 438B

Proprietary

Release date

Claude Mythos 5.2

2026-11-09

Quasar 438B

2026-09-02

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: $0.52 vs $0.0354. Cache-heavy agent loop: $0.6 vs $0.15.
Context tradeoff
Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Mythos 5.2 or Quasar 438B?

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 Mythos 5.2 or Quasar 438B?

Claude Mythos 5.2 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Claude Mythos 5.2 or Quasar 438B?

Claude Mythos 5.2 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Claude Mythos 5.2 or Quasar 438B?

For the stated presets, chat costs $0.028 on Claude Mythos 5.2 and $0.0015 on Quasar 438B; repository review costs $0.52 and $0.0354; the cache-heavy agent loop costs $0.6 and $0.15. Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Claude Mythos 5.2 or Quasar 438B?

Both models list the same context window, 1M.

Benchmark evidence

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

Browse raw public benchmark evidence16 rows

Agentic

  • Terminal-Bench 4.0

    Claude Mythos 5.270.50%
    Source
    Quasar 438B—

    Not directly comparable

  • Terminal-Bench-Science 0.1

    Claude Mythos 5.275.3%
    Source
    Quasar 438B—

    Not directly comparable

  • CVE Rediscovery

    Claude Mythos 5.289.6%
    Source
    Quasar 438B—

    Not directly comparable

  • ExploitGym

    Claude Mythos 5.238.1%
    Source
    Quasar 438B—

    Not directly comparable

  • Binary Exploitation Benchmark

    Claude Mythos 5.2148
    Source
    Quasar 438B—

    Not directly comparable

  • Terminal-Bench 2.1

    Claude Mythos 5.2—
    Quasar 438B69.3%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Claude Mythos 5.2—
    Quasar 438B69.3%
    Source

    Not directly comparable

Multimodal

  • Biomedical image analysis

    Claude Mythos 5.280.7%
    Source
    Quasar 438B—

    Not directly comparable

Knowledge

  • De novo protein-binder design

    Claude Mythos 5.285.1%
    Source
    Quasar 438B—

    Not directly comparable

  • Protein Design

    Claude Mythos 5.264.6%
    Source
    Quasar 438B—

    Not directly comparable

  • SingleCellBench

    Claude Mythos 5.265.6%
    Source
    Quasar 438B—

    Not directly comparable

  • Protocols (troubleshooting)

    Claude Mythos 5.277.1%
    Source
    Quasar 438B—

    Not directly comparable

  • Protein Domain ID

    Claude Mythos 5.291.8%
    Source
    Quasar 438B—

    Not directly comparable

  • Protein Mutation Attribution

    Claude Mythos 5.251.5%
    Source
    Quasar 438B—

    Not directly comparable

  • Morphology-to-molecule matching

    Claude Mythos 5.235.2%
    Source
    Quasar 438B—

    Not directly comparable

  • Medicinal chemistry

    Claude Mythos 5.269.0%
    Source
    Quasar 438B—

    Not directly comparable

16 public results · 0 shared

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Last updated November 10, 2026