Agentic
Directional only- Apodex 1.1
- 53.7
- Estimated · #39/152
- Mercury 2.5
- 44.5
- Estimated · #91/152
- Basis
- BenchAlign lane · 4 vs 2 public rows
- Reading
- Directional only
Apodex 1.1 vs Mercury 2.5
Updated September 8, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
Share or export
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Apodex 1.1 and Mercury 2.5 are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Apodex 1.1 and Mercury 2.5 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
Confidence: limited
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.
9 Apodex 1.1 only
1 Shared
4 Mercury 2.5 only
- Shared results
- 1
- Apodex 1.1 only
- 9
- Mercury 2.5 only
- 4
- Like-for-like categories
- 0 / 8
3 categories rest 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.
Coding
Directional only- Apodex 1.1
- 52.9
- Estimated · #46/151
- Mercury 2.5
- 47.8
- Estimated · #73/151
- Basis
- BenchAlign lane · 2 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- Apodex 1.1
- 54.9
- Supported · #55/182
- Mercury 2.5
- 48.3
- Estimated · #94/182
- Basis
- BenchAlign lane · 3 vs 1 public rows
- Reading
- Directional only
Reasoning
Not comparable- Apodex 1.1
- 75.5
- Unranked · 2 rankable rows
- Mercury 2.5
- 67.7
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Apodex 1.1
- Not ranked
- Mercury 2.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Apodex 1.1
- Not ranked
- Mercury 2.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Apodex 1.1
- 76.6
- Unranked · 1 rankable row
- Mercury 2.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Apodex 1.1
- Not ranked
- Mercury 2.5
- 80.1
- #53/121
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | Apodex 1.1 | Mercury 2.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 53.7Estimated · #39/152 | 44.5Estimated · #91/152 | Directional onlyBenchAlign lane · 4 vs 2 public rows | Directional only |
| Coding | 52.9Estimated · #46/151 | 47.8Estimated · #73/151 | Directional onlyBenchAlign lane · 2 vs 1 public rows | Directional only |
| Knowledge | 54.9Supported · #55/182 | 48.3Estimated · #94/182 | Directional onlyBenchAlign lane · 3 vs 1 public rows | Directional only |
| Reasoning | 75.5Unranked · 2 rankable rows | 67.7Unranked · 1 rankable row | 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 |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 76.6Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 80.1#53/121 | Not comparableProvisional lane · 0 vs 1 weighted rows | 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
- Apodex 1.1
- API rate not published
- Fit state unavailable
- Mercury 2.5
- $0.00012
- Fits in one request
Apodex 1.1 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Apodex 1.1
- API rate not published
- Fit state unavailable
- Mercury 2.5
- $0.00245
- Fits in one request
Apodex 1.1 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Apodex 1.1
- API rate not published
- Fit state unavailable
- Cached-input rate unavailable
- Mercury 2.5
- $0.0031
- Fits in one request
Apodex 1.1 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.
SpecificationApodex 1.1Mercury 2.5
Context window
Maximum documented context; output-token limits may be lower.
Apodex 1.1
N/A
Mercury 2.5
API model ID
Apodex 1.1
Not sourced
Mercury 2.5
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Apodex 1.1
No comparable hosted API rate
Apodex pricingMercury 2.5
$0.004 per 1M cached input tokens
Inception models and Mercury 2.5 launch pricingDocumented inputs
Apodex 1.1
Not sourced
Mercury 2.5
Not sourced
Documented outputs
Apodex 1.1
Not sourced
Mercury 2.5
Not sourced
Provider availability
Apodex 1.1
Not sourced
Mercury 2.5
Not sourced
Reasoning profile
Apodex 1.1
Reasoning
Mercury 2.5
Reasoning
Weight access
Apodex 1.1
Proprietary
Mercury 2.5
Proprietary
License
Apodex 1.1
Proprietary
Mercury 2.5
Proprietary
Release date
Apodex 1.1
2026-08-24
Mercury 2.5
2026-09-08
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
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
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
Terminal-Bench 2.1
Apodex 1.170.8%SourceMercury 2.5—Not directly comparable
HLE w/ tools
Apodex 1.156.1%SourceMercury 2.5—Not directly comparable
APEX-Agents
Apodex 1.138.5%SourceMercury 2.5—Not directly comparable
DeepSearchQA
Apodex 1.192.4%SourceMercury 2.534.0%SourceApodex 1.1 leads this result
τ³-bench results
Apodex 1.1—Mercury 2.596.0%SourceNot directly comparable
Coding
Knowledge
Math
IMO 2026
Apodex 1.131/42SourceMercury 2.5—Not directly comparable
Instruction following
IFBench
Apodex 1.1—Mercury 2.577%SourceNot directly comparable
Frequently asked questions
Which is better, Apodex 1.1 or Mercury 2.5?
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, Apodex 1.1 or Mercury 2.5?
Apodex 1.1 scores higher for coding on the public lane, 52.9 to 47.8. Apodex 1.1 and Mercury 2.5 are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Apodex 1.1 or Mercury 2.5?
Apodex 1.1 scores higher for agentic tasks on the public lane, 53.7 to 44.5. Apodex 1.1 and Mercury 2.5 are 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, Apodex 1.1 or Mercury 2.5?
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, Apodex 1.1 or Mercury 2.5?
A complete documented context-window comparison is not available.
Related comparisons
Last updated September 8, 2026
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