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BenchLM

Claude Haiku 4.5 vs Gemini 2.5 Pro

Updated September 29, 2026. Rank says Gemini 2.5 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Gemini 2.5 Pro has the higher public score estimate, 50.17 versus 42.47, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 4 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

42.47/100

Estimated · Public rank #113

90% interval 30.9–54.0

Model B
Google logo

Google

50.17/100

Supported · Public rank #80

90% interval 35.9–64.4

Shared results
4
Claude Haiku 4.5 only
6
Gemini 2.5 Pro only
4
Like-for-like categories
1 / 8
Estimated: Claude Haiku 4.5 · Supported: Gemini 2.5 ProHow 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.

  • Coding work

    Code generation, repair, and software-engineering tasks

    Gemini 2.5 Pro

    Gemini 2.5 Pro leads on the public coding lane, 24.4 to 19.6, with Supported evidence for both models, although the 90% intervals overlap.

    Confidence: limited
  • Long documents

    Prompts that approach the documented context limit

    Gemini 2.5 Pro

    Gemini 2.5 Pro has the larger documented context window.

    Confidence: documented
  • Chat turn cost

    1K fresh input + 500 output tokens

    Claude Haiku 4.5

    Claude Haiku 4.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    Claude Haiku 4.5

    Claude Haiku 4.5 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates
  • Agentic work

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

    Not enough matched evidence

    Gemini 2.5 Pro is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited
  • 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 Haiku 4.5 does not fit this workload in one request.

    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.

19.6Claude Haiku 4.524.4Gemini 2.5 Pro

Like-for-like · BenchAlign v5.7

Gemini 2.5 Pro leads the like-for-like coding row, although the 90% intervals overlap.

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.

2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

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.

Coding

Like-for-like
Claude Haiku 4.5
19.6
Supported · #124/143
Gemini 2.5 Pro
24.4
Supported · #108/143
Basis
BenchAlign v5.7 lane · 4 vs 3 public rows
Reading
Gemini 2.5 Pro leads · intervals overlap

Agentic

Directional only
Claude Haiku 4.5
22.0
Supported · #96/117
Gemini 2.5 Pro
25.4
Estimated · #91/117
Basis
BenchAlign v5.7 lane · 2 vs 1 public rows
Reading
Directional only

Knowledge

Directional only
Claude Haiku 4.5
34.8
Estimated · #119/169
Gemini 2.5 Pro
44.1
Supported · #80/169
Basis
BenchAlign v5.7 lane · 2 vs 2 public rows
Reading
Directional only

Reasoning

Not comparable
Claude Haiku 4.5
Not ranked
Gemini 2.5 Pro
69.7
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Claude Haiku 4.5
Not ranked
Gemini 2.5 Pro
71.4
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Claude Haiku 4.5
Not ranked
Gemini 2.5 Pro
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Claude Haiku 4.5
Not ranked
Gemini 2.5 Pro
56.4
#76/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Claude Haiku 4.5
28.8
Unranked · 2 rankable rows
Gemini 2.5 Pro
35.1
Unranked · 2 rankable rows
Basis
Provisional lane · 2 vs 2 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.

Supported evidence per lane · 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 Haiku 4.5
$0.0035
Fits in one request
Gemini 2.5 Pro
$0.00625
Fits in one request

Claude Haiku 4.5 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Claude Haiku 4.5
$0.065
Fits in one request
Gemini 2.5 Pro
$0.0925
Fits in one request

Claude Haiku 4.5 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 Haiku 4.5
$0.09
Does not fit in one request
Gemini 2.5 Pro
$0.15
Fits in one request

Claude Haiku 4.5 does not fit this workload in one request.

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.

Cached-input rate

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

Claude Haiku 4.5

$0.1 per 1M cached input tokens

Claude API pricing

Gemini 2.5 Pro

$0.125 per 1M cached input tokens

Google Gemini API pricing

Documented inputs

Claude Haiku 4.5

Not sourced

Gemini 2.5 Pro

Not sourced

Documented outputs

Claude Haiku 4.5

Not sourced

Gemini 2.5 Pro

Not sourced

Provider availability

Claude Haiku 4.5

Not sourced

Gemini 2.5 Pro

Not sourced

Reasoning profile

Claude Haiku 4.5

Non-Reasoning

Gemini 2.5 Pro

Non-Reasoning

Weight access

Claude Haiku 4.5

Proprietary

Gemini 2.5 Pro

Proprietary

License

Claude Haiku 4.5

Proprietary

Gemini 2.5 Pro

Proprietary

Release date

Claude Haiku 4.5

2025-10-15

Gemini 2.5 Pro

2025-03-01

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
Gemini 2.5 Pro has the higher public score estimate, 50.17 versus 42.47, but the 90% score intervals overlap.
Workload cost
Repository review: $0.065 vs $0.0925. Cache-heavy agent loop: $0.09 vs $0.15.
Context tradeoff
Gemini 2.5 Pro has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Claude Haiku 4.5 or Gemini 2.5 Pro?

Gemini 2.5 Pro has the higher public score estimate, 50.17 versus 42.47, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Claude Haiku 4.5 or Gemini 2.5 Pro?

Gemini 2.5 Pro leads the public coding lane, 24.4 to 19.6, with Supported evidence for both models, although the 90% intervals overlap.

Which is better for agentic tasks, Claude Haiku 4.5 or Gemini 2.5 Pro?

Gemini 2.5 Pro scores higher for agentic tasks on the public lane, 25.4 to 22. Gemini 2.5 Pro is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Claude Haiku 4.5 or Gemini 2.5 Pro?

For the stated presets, chat costs $0.0035 on Claude Haiku 4.5 and $0.00625 on Gemini 2.5 Pro; repository review costs $0.065 and $0.0925; the cache-heavy agent loop costs $0.09 and $0.15. Claude Haiku 4.5 does not fit this workload in one request.

Which has the larger context window, Claude Haiku 4.5 or Gemini 2.5 Pro?

Gemini 2.5 Pro has the larger documented context window: 1M, compared with 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 evidence14 rows

Agentic

  • JobBench

    Claude Haiku 4.516.0%
    Source
    Gemini 2.5 Pro—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Claude Haiku 4.543.8%
    Source
    Gemini 2.5 Pro—

    Not directly comparable

  • Gert Labs

    Claude Haiku 4.5—
    Gemini 2.5 Pro42.01%
    Source

    Not directly comparable

Coding

  • SWE-bench Verified

    Claude Haiku 4.573.3%
    Source
    Gemini 2.5 Pro63.8%
    Source

    Claude Haiku 4.5 leads this result

  • VulcanBench v3

    Claude Haiku 4.576.2%
    Source
    Gemini 2.5 Pro—

    Not directly comparable

  • LiveCodeBench (Vals)

    Claude Haiku 4.541.2%
    Source
    Gemini 2.5 Pro—

    Not directly comparable

  • SWE-bench (Vals)

    Claude Haiku 4.566.6%
    Source
    Gemini 2.5 Pro54.4%
    Source

    Claude Haiku 4.5 leads this result

  • Vibe Code Bench

    Claude Haiku 4.5—
    Gemini 2.5 Pro0.40%
    Source

    Not directly comparable

Knowledge

  • GPQA Diamond (Vals)

    Claude Haiku 4.572.2%
    Source
    Gemini 2.5 Pro—

    Not directly comparable

  • MMLU-Pro (Vals)

    Claude Haiku 4.578.7%
    Source
    Gemini 2.5 Pro—

    Not directly comparable

  • GPQA

    Claude Haiku 4.5—
    Gemini 2.5 Pro83%
    Source

    Not directly comparable

  • HLE

    Claude Haiku 4.5—
    Gemini 2.5 Pro18.8%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Shared source
    Claude Haiku 4.55.903%
    Gemini 2.5 Pro14.138%

    Gemini 2.5 Pro leads this result

  • FrontierMath v2 (Tier 4)

    Shared source
    Claude Haiku 4.52.083%
    Gemini 2.5 Pro4.167%

    Gemini 2.5 Pro leads this result

14 public results · 4 shared

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Last updated September 29, 2026