Long documents
Prompts that approach the documented context limit
GPT-5.5 Pro
GPT-5.5 Pro has the larger documented context window.
Confidence: documented
GPT-5.5 Pro vs Mercury 3
Updated October 19, 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.
- Shared results
- 0
- GPT-5.5 Pro only
- 8
- Mercury 3 only
- 8
- Like-for-like categories
- 0 / 8
Estimated: GPT-5.5 Pro. Conditional ranges do not establish rank confidence.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
Mercury 3
Mercury 3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Mercury 3
Mercury 3 has the lower estimated token cost for this stated workload. GPT-5.5 Pro 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
Mercury 3
Mercury 3 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-5.5 Pro and Mercury 3 are not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.5 Pro and Mercury 3 are 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.
—GPT-5.5 Pro—Mercury 3
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.
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.
8 GPT-5.5 Pro only
8 Mercury 3 only
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, 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- GPT-5.5 Pro
- Not ranked
- Mercury 3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 1 vs 3 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.5 Pro
- Not ranked
- Mercury 3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 0 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.5 Pro
- 78.9
- Unranked · 3 rankable rows
- Mercury 3
- Not ranked
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.5 Pro
- Not ranked
- Mercury 3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.5 Pro
- Not ranked
- Mercury 3
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.5 Pro
- Not ranked
- Mercury 3
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.5 Pro
- Not ranked
- Mercury 3
- Not ranked
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5 Pro
- 94.0
- Unranked · 3 rankable rows
- Mercury 3
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.5 Pro | Mercury 3 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 1 vs 3 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 1 public rows | Not comparable |
| Reasoning | 78.9Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 1 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | 94.0Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, 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
- GPT-5.5 Pro
- $0.12
- Fits in one request
- Mercury 3
- $0.00028
- Fits in one request
Mercury 3 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.5 Pro
- $2.04
- Fits in one request
- Mercury 3
- $0.00605
- Fits in one request
Mercury 3 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.5 Pro
- $8.40
- Fits in one request
- Cached input priced at the published list-input rate
- Mercury 3
- $0.0075
- Fits in one request
Mercury 3 has the lower modeled cost
GPT-5.5 Pro has no published cached-input rate, so cached tokens use its listed input rate.
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.
SpecificationGPT-5.5 ProMercury 3
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5 Pro
API model ID
GPT-5.5 Pro
gpt-5.5-pro
OpenAI GPT-5.5 Pro model documentationMercury 3
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.5 Pro
Not published
OpenAI pricingMercury 3
$0.01 per 1M cached input tokens
Inception Mercury 3 models page and launch pricingDocumented inputs
GPT-5.5 Pro
text, image
OpenAI model catalogMercury 3
Not sourced
Documented outputs
GPT-5.5 Pro
Mercury 3
Not sourced
Provider availability
GPT-5.5 Pro
Generally Available · OpenAI Responses API
OpenAI model catalogMercury 3
Not sourced
Reasoning profile
GPT-5.5 Pro
Reasoning
Mercury 3
Reasoning
Weight access
GPT-5.5 Pro
Proprietary
Mercury 3
Proprietary
License
GPT-5.5 Pro
Proprietary
Mercury 3
Proprietary
Release date
GPT-5.5 Pro
2026-04-23
Mercury 3
2026-10-19
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: $2.04 vs $0.00605. Cache-heavy agent loop: $8.40 vs $0.0075.
- Context tradeoff
- GPT-5.5 Pro has the larger documented window (1.05M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.5 Pro or Mercury 3?
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, GPT-5.5 Pro or Mercury 3?
GPT-5.5 Pro and Mercury 3 are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.5 Pro or Mercury 3?
GPT-5.5 Pro and Mercury 3 are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.5 Pro or Mercury 3?
For the stated presets, chat costs $0.12 on GPT-5.5 Pro and $0.00028 on Mercury 3; repository review costs $2.04 and $0.00605; the cache-heavy agent loop costs $8.40 and $0.0075. GPT-5.5 Pro has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.5 Pro or Mercury 3?
GPT-5.5 Pro has the larger documented context window: 1.05M, 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 evidence16 rows
Agentic
Coding
DeepSWE
GPT-5.5 Pro—Mercury 346.3%SourceNot directly comparable
Reasoning
Knowledge
Instruction following
Math
16 public results · 0 shared
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Last updated October 19, 2026