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BenchLM

Ember-1 vs Gemini 2.5 Pro

Updated September 28, 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

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

Fireworks

—

Evidence status unavailable

90% interval unavailable

Model B
Google logo

Google

50.23/100

Supported · Public rank #75

90% interval 36.1–64.4

Shared results
1
Ember-1 only
4
Gemini 2.5 Pro only
7
Like-for-like categories
0 / 8
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.

  • Long documents

    Prompts that approach the documented context limit

    Ember-1

    Ember-1 has the larger documented context window.

    Confidence: documented
  • Chat turn cost

    1K fresh input + 500 output tokens

    Gemini 2.5 Pro

    Gemini 2.5 Pro 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

    Gemini 2.5 Pro

    Gemini 2.5 Pro 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

    Gemini 2.5 Pro

    Gemini 2.5 Pro 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

    Ember-1 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

    Ember-1 is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

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.

—Ember-124.4Gemini 2.5 Pro

Not comparable · BenchAlign v5.7

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.

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.

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.

Agentic

Not comparable
Ember-1
Not ranked
Gemini 2.5 Pro
24.8
Estimated · #86/111
Basis
BenchAlign v5.7 lane · 2 vs 1 public rows
Reading
Not comparable

Coding

Not comparable
Ember-1
Not ranked
Gemini 2.5 Pro
24.4
Supported · #102/136
Basis
BenchAlign v5.7 lane · 3 vs 3 public rows
Reading
Not comparable

Reasoning

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

Knowledge

Not comparable
Ember-1
Not ranked
Gemini 2.5 Pro
44.3
Supported · #75/160
Basis
BenchAlign v5.7 lane · 0 vs 2 public rows
Reading
Not comparable

Multilingual

Not comparable
Ember-1
Not ranked
Gemini 2.5 Pro
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Ember-1
Not ranked
Gemini 2.5 Pro
56.4
#76/124
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Ember-1
Not ranked
Gemini 2.5 Pro
35.1
Unranked · 2 rankable rows
Basis
Provisional lane · 0 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

Ember-1
$0.0105
Fits in one request
Gemini 2.5 Pro
$0.00625
Fits in one request

Gemini 2.5 Pro has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Ember-1
$0.195
Fits in one request
Gemini 2.5 Pro
$0.0925
Fits in one request

Gemini 2.5 Pro has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Ember-1
$0.27
Fits in one request
Gemini 2.5 Pro
$0.15
Fits in one request

Gemini 2.5 Pro has the lower modeled cost

Costs use the listed standard API rates.

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.

Documented inputs

Ember-1

Not sourced

Gemini 2.5 Pro

Not sourced

Documented outputs

Ember-1

Not sourced

Gemini 2.5 Pro

Not sourced

Provider availability

Ember-1

Not sourced

Gemini 2.5 Pro

Not sourced

Reasoning profile

Ember-1

Reasoning

Gemini 2.5 Pro

Non-Reasoning

Weight access

Ember-1

Proprietary

Gemini 2.5 Pro

Proprietary

License

Ember-1

Proprietary

Gemini 2.5 Pro

Proprietary

Release date

Ember-1

2026-09-23

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $0.195 vs $0.0925. Cache-heavy agent loop: $0.27 vs $0.15.
Context tradeoff
Ember-1 has the larger documented window (1.04M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Ember-1 or Gemini 2.5 Pro?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Ember-1 or Gemini 2.5 Pro?

Ember-1 is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Ember-1 or Gemini 2.5 Pro?

Ember-1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Ember-1 or Gemini 2.5 Pro?

For the stated presets, chat costs $0.0105 on Ember-1 and $0.00625 on Gemini 2.5 Pro; repository review costs $0.195 and $0.0925; the cache-heavy agent loop costs $0.27 and $0.15. Costs use the listed standard API rates.

Which has the larger context window, Ember-1 or Gemini 2.5 Pro?

Ember-1 has the larger documented context window: 1.04M, compared with 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 evidence12 rows

Agentic

  • Terminal-Bench 2.1

    Ember-182.0%
    Source
    Gemini 2.5 Pro—

    Not directly comparable

  • τ²-bench Airline

    Ember-166.0%
    Source
    Gemini 2.5 Pro—

    Not directly comparable

  • Gert Labs

    Ember-1—
    Gemini 2.5 Pro42.01%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    Ember-182.0%
    Source
    Gemini 2.5 Pro—

    Not directly comparable

  • SWE-bench Verified

    Ember-192.2%
    Source
    Gemini 2.5 Pro63.8%
    Source

    Ember-1 leads this result

  • DeepSWE

    Ember-175.2%
    Source
    Gemini 2.5 Pro—

    Not directly comparable

  • Vibe Code Bench

    Ember-1—
    Gemini 2.5 Pro0.40%
    Source

    Not directly comparable

  • SWE-bench (Vals)

    Ember-1—
    Gemini 2.5 Pro54.4%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Ember-1—
    Gemini 2.5 Pro83%
    Source

    Not directly comparable

  • HLE

    Ember-1—
    Gemini 2.5 Pro18.8%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Ember-1—
    Gemini 2.5 Pro14.138%
    Source

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Ember-1—
    Gemini 2.5 Pro4.167%
    Source

    Not directly comparable

12 public results · 1 shared

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Last updated September 28, 2026