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BenchLM
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Model A
Claude Opus 5

Anthropic

80.66/100

Supported · Public rank #4

90% interval 78.3–83.0

Claude Opus 5 vs Gemma 4 31B

Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Google logo
Model B
Gemma 4 31B

Google

58.69/100

Supported · Public rank #83

90% interval 42.8–74.6

Decision reading

Claude Opus 5 has the higher public score, 80.66 versus 58.69, and the 90% score intervals do not overlap.

2 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Claude Opus 5

    Claude Opus 5 leads on the public coding lane, 75.6 to 43.5, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Claude Opus 5

    Claude Opus 5 leads on the public agentic lane, 77.4 to 27.6, with Supported evidence for both models and non-overlapping 90% intervals.

    Confidence: stronger

  • Long documents

    Prompts that approach the documented context limit

    Claude Opus 5

    Claude Opus 5 has the larger documented context window.

    Confidence: documented

Show secondary and unsupported calls

  • 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
2
Claude Opus 5 only
71
Gemma 4 31B only
6
Like-for-like categories
3 / 8

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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

Like-for-like
Claude Opus 5
77.4
Supported · #2/151
Gemma 4 31B
27.6
Supported · #140/151
Basis
BenchAlign lane · 19 vs 1 public rows
Reading
Claude Opus 5 leads

Coding

Like-for-like
Claude Opus 5
75.6
Supported · #3/183
Gemma 4 31B
43.5
Supported · #123/183
Basis
BenchAlign lane · 16 vs 2 public rows
Reading
Claude Opus 5 leads

Knowledge

Like-for-like
Claude Opus 5
82.1
Supported · #3/181
Gemma 4 31B
47.9
Supported · #104/181
Basis
BenchAlign lane · 19 vs 4 public rows
Reading
Claude Opus 5 leads

Multimodal

Directional only
Claude Opus 5
88.7
#2/48
Gemma 4 31B
58.4
#30/48
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Directional only

Reasoning

Not comparable
Claude Opus 5
75.6
#13/22
Gemma 4 31B
70.1
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Opus 5
Not ranked
Gemma 4 31B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Opus 5
Not ranked
Gemma 4 31B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Opus 5
Not ranked
Gemma 4 31B
92.6
#12/120
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.

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 Opus 5
$0.0175
Fits in one request
Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 31B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Claude Opus 5
$0.325
Fits in one request
Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request

Gemma 4 31B has no comparable published API token rate.

Cache-heavy agent loop

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

Claude Opus 5
$0.45
Fits in one request
Gemma 4 31B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Gemma 4 31B 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 Opus 5

$0.5 per 1M cached input tokens

Claude API pricing

Gemma 4 31B

No comparable hosted API rate

Reasoning profile

Claude Opus 5

Reasoning

Gemma 4 31B

Reasoning

Weight access

Claude Opus 5

Proprietary

Gemma 4 31B

Open Weight

License

Claude Opus 5

Proprietary

Gemma 4 31B

Open Weight

Release date

Claude Opus 5

2026-07-24

Gemma 4 31B

2026-04-02

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
Claude Opus 5 has the higher public score, 80.66 versus 58.69, and the 90% score intervals do not overlap.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Claude Opus 5 has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Claude Opus 5
API / mo$22,500
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Gemma 4 31B
API / mo$0
Self-host / mo$429
Break-even—
Model the full break-even

Benchmark evidence

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

Browse raw public benchmark evidence79 rows

Agentic

  • Terminal-Bench 3.0

    Claude Opus 542.7%
    Source
    Gemma 4 31B—

    Not directly comparable

  • BrowseComp

    Claude Opus 590.8%
    Source
    Gemma 4 31B—

    Not directly comparable

  • HLE w/ tools

    Claude Opus 564.7%
    Source
    Gemma 4 31B—

    Not directly comparable

  • DeepSearchQA

    Claude Opus 595.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • DRACO

    Claude Opus 588.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • BrowseComp (10-agent, prerelease)

    Claude Opus 593.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • OSWorld 2.0

    Claude Opus 570.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • MCP Atlas

    Claude Opus 585.8%
    Source
    Gemma 4 31B—

    Not directly comparable

  • MCP-Atlas claim coverage

    Claude Opus 589.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • LAB all-pass (Anthropic harness)

    Claude Opus 523.58%
    Source
    Gemma 4 31B—

    Not directly comparable

  • LAB criterion-pass (Anthropic harness)

    Claude Opus 593.74%
    Source
    Gemma 4 31B—

    Not directly comparable

  • LAB all-pass (Harvey held-out)

    Claude Opus 511.7%
    Source
    Gemma 4 31B—

    Not directly comparable

  • LAB criterion-pass (Harvey held-out)

    Claude Opus 594.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Toolathlon-Verified

    Claude Opus 580.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Toolathlon Verified Pass@3

    Claude Opus 587.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Toolathlon Verified Pass³

    Claude Opus 573.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Toolathlon Verified avg. turns

    Claude Opus 523.5 turns
    Source
    Gemma 4 31B—

    Not directly comparable

  • AutomationBench

    Claude Opus 526.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Opus 584.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Gert Labs

    Claude Opus 5—
    Gemma 4 31B35.26%
    Source

    Not directly comparable

Coding

  • Bug Hunt Bench

    Claude Opus 527 fixes
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE-bench Verified

    Claude Opus 596%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE-bench Pro

    Claude Opus 579.2%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE Multilingual

    Claude Opus 589.5%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE Multimodal

    Claude Opus 559.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • deepSwe

    Claude Opus 568.8%
    Source
    Gemma 4 31B—

    Not directly comparable

  • FrontierCode 1.1 Main

    Claude Opus 553.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • FrontierCode 1.1 Extended

    Claude Opus 563.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • FrontierSWE v2

    Claude Opus 552.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • ProgramBench (episode 1)

    Claude Opus 583.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • ProgramBench

    Claude Opus 593.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • cursorBench32

    Claude Opus 570.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • VulcanBench v3

    Claude Opus 587.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • VulcanBench CII v1

    Claude Opus 596.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Opus 589.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Opus 597.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SWE-Rebench

    Claude Opus 5—
    Gemma 4 31B41.6%
    Source

    Not directly comparable

  • React Native Evals

    Claude Opus 5—
    Gemma 4 31B75.2%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-1

    Claude Opus 597.50%
    Source
    Gemma 4 31B—

    Not directly comparable

  • ARC-AGI-2

    Claude Opus 590.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • ARC-AGI-3

    Claude Opus 530.2%
    Source
    Gemma 4 31B—

    Not directly comparable

Knowledge

  • HLE

    Claude Opus 564.7%
    Source
    Gemma 4 31B26.5%
    Source

    Claude Opus 5 leads this result

  • HLE w/o tools

    Claude Opus 556.3%
    Source
    Gemma 4 31B19.5%
    Source

    Claude Opus 5 leads this result

  • HLE-Verified

    Claude Opus 554.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • LABBench2

    Claude Opus 584.2%
    Source
    Gemma 4 31B—

    Not directly comparable

  • HealthBench (raw)

    Claude Opus 567.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • HealthBench (length-adjusted)

    Claude Opus 557.8%
    Source
    Gemma 4 31B—

    Not directly comparable

  • HealthBench Professional

    Claude Opus 559.8%
    Source
    Gemma 4 31B—

    Not directly comparable

  • HealthBench Professional (raw)

    Claude Opus 573.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • BioMysteryBench (human-solvable)

    Claude Opus 590.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • BioMysteryBench (human-difficult)

    Claude Opus 549.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SpatialBench Verified

    Claude Opus 572.5%
    Source
    Gemma 4 31B—

    Not directly comparable

  • SingleCellBench

    Claude Opus 560.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • ProteinGym Hard

    Claude Opus 547.7%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Protein Design

    Claude Opus 542.5%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Organic chemistry V2

    Claude Opus 561.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Protocols (troubleshooting)

    Claude Opus 561.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Protocols (understanding)

    Claude Opus 578.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • GPQA Diamond (Vals)

    Claude Opus 593.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Opus 591.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • GPQA

    Claude Opus 5—
    Gemma 4 31B84.3%
    Source

    Not directly comparable

  • MMLU-Pro

    Claude Opus 5—
    Gemma 4 31B85.2%
    Source

    Not directly comparable

Math

  • IMO 2026

    Claude Opus 542/42
    Source
    Gemma 4 31B—

    Not directly comparable

  • RiemannBench (no tools)

    Claude Opus 560.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • RiemannBench (tools)

    Claude Opus 579.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • ArXivMath Jun. 2026 (no tools)

    Claude Opus 590.8%
    Source
    Gemma 4 31B—

    Not directly comparable

  • ArXivMath Jun. 2026 (tools)

    Claude Opus 591.3%
    Source
    Gemma 4 31B—

    Not directly comparable

Multilingual

  • GMMLU

    Claude Opus 592.5%
    Source
    Gemma 4 31B—

    Not directly comparable

  • MILU

    Claude Opus 592.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • INCLUDE

    Claude Opus 589.8%
    Source
    Gemma 4 31B—

    Not directly comparable

Multimodal

  • Chartography (no tools)

    Claude Opus 529.6%
    Source
    Gemma 4 31B—

    Not directly comparable

  • Chartography (tools)

    Claude Opus 583.0%
    Source
    Gemma 4 31B—

    Not directly comparable

  • BenchCAD Vision2Code (no tools)

    Claude Opus 50.366
    Source
    Gemma 4 31B—

    Not directly comparable

  • BenchCAD Vision2Code (tools)

    Claude Opus 50.821
    Source
    Gemma 4 31B—

    Not directly comparable

  • GDP.pdf (no tools)

    Claude Opus 583.4%
    Source
    Gemma 4 31B—

    Not directly comparable

  • GDP.pdf (tools)

    Claude Opus 585.5%
    Source
    Gemma 4 31B—

    Not directly comparable

  • OfficeQA

    Claude Opus 578.1%
    Source
    Gemma 4 31B—

    Not directly comparable

  • OfficeQA Pro

    Claude Opus 566.9%
    Source
    Gemma 4 31B—

    Not directly comparable

  • MMMU-Pro

    Claude Opus 5—
    Gemma 4 31B76.9%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Claude Opus 5 or Gemma 4 31B?

Claude Opus 5 has the higher public score, 80.66 versus 58.69, 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 Opus 5 or Gemma 4 31B?

Claude Opus 5 leads the public coding lane, 75.6 to 43.5, with Supported evidence for both models and non-overlapping 90% intervals.

Which is better for agentic tasks, Claude Opus 5 or Gemma 4 31B?

Claude Opus 5 leads the public agentic tasks lane, 77.4 to 27.6, with Supported evidence for both models and non-overlapping 90% intervals.

Which costs less, Claude Opus 5 or Gemma 4 31B?

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 Opus 5 or Gemma 4 31B?

Claude Opus 5 has the larger documented context window: 1M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 4, 2026

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