Updated November 11, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
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.
Mercury Decide: $0.040 input / $0 output
SubQ Snap 1.1: $0.040 input / $0 output
Mercury Decide: Not sourced
SubQ Snap 1.1: 384K
Mercury Decide: Not sourced
SubQ Snap 1.1: October 22, 2026
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.
200K cached + 20K fresh input + 10K output tokens
Mercury Decide
Mercury Decide has the lower estimated token cost for this stated workload. SubQ Snap 1.1 has no published cached-input rate, so cached tokens use its listed input rate.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Mercury Decide and SubQ Snap 1.1 are not ranked on the public lane for coding, so no winner is named for coding.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Mercury Decide and SubQ Snap 1.1 are not ranked on the public lane for agentic, so no winner is named for agentic.
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
50K fresh input + 3K output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
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.
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.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
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
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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.
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.
| Category | Mercury Decide | SubQ Snap 1.1 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Reasoning | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 public rows | Not comparable |
| Knowledge | Not ranked | Not ranked | Not comparableBenchAlign v5.8 lane · 0 vs 0 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 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | 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.
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.
1K fresh input + 500 output tokens
Modeled costs are equal
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Modeled costs are equal
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Mercury Decide has the lower modeled cost
SubQ Snap 1.1 has no published cached-input rate, so cached tokens use its listed input rate.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Mercury Decide
Not sourced
SubQ Snap 1.1
Mercury Decide
mercury-decide
Inception models and pricingSubQ Snap 1.1
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Mercury Decide
$0 per 1M cached input tokens
Inception models and pricingSubQ Snap 1.1
Not published
Subquadratic pricingMercury Decide
Not sourced
SubQ Snap 1.1
Not sourced
Mercury Decide
Not sourced
SubQ Snap 1.1
Not sourced
Mercury Decide
Not sourced
SubQ Snap 1.1
Not sourced
Mercury Decide
Unspecified
SubQ Snap 1.1
Unspecified
Mercury Decide
Proprietary
SubQ Snap 1.1
Proprietary
Mercury Decide
Proprietary
SubQ Snap 1.1
Proprietary
Mercury Decide
Not sourced
SubQ Snap 1.1
2026-10-22
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.
Mercury Decide and SubQ Snap 1.1 are not ranked on the public lane for coding, so no winner is named for coding.
Mercury Decide and SubQ Snap 1.1 are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
For the stated presets, chat costs $0.00004 on Mercury Decide and $0.00004 on SubQ Snap 1.1; repository review costs $0.002 and $0.002; the cache-heavy agent loop costs $0.0008 and $0.0088. SubQ Snap 1.1 has no published cached-input rate, so cached tokens use its listed input rate.
A complete documented context-window comparison is not available.
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 7,000+ subscribers.
Last updated November 11, 2026
