Coding
Like-for-like- Grok 4.20
- 28.2
- Supported · #141/151
- Laguna XS.2
- 24.7
- Supported · #148/151
- Basis
- BenchAlign lane · 6 vs 6 public rows
- Reading
- Grok 4.20 leads · intervals overlap
Grok 4.20 vs Laguna XS.2
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Grok 4.20 has the higher public score, 67.13 versus 1.53, and the 90% score intervals do not overlap.
8 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.
Coding work
Code generation, repair, and software-engineering tasks
Grok 4.20
Grok 4.20 leads on the public coding lane, 28.2 to 24.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Grok 4.20
Grok 4.20 has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Laguna XS.2 is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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: rate-fallback
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.
15 Grok 4.20 only
8 Shared
2 Laguna XS.2 only
- Shared results
- 8
- Grok 4.20 only
- 15
- Laguna XS.2 only
- 2
- Like-for-like categories
- 1 / 8
2 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.
Agentic
Directional only- Grok 4.20
- 26.7
- Supported · #145/152
- Laguna XS.2
- 22.4
- Estimated · #149/152
- Basis
- BenchAlign lane · 4 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- Grok 4.20
- 49.4
- Supported · #85/183
- Laguna XS.2
- 17.7
- Estimated · #183/183
- Basis
- BenchAlign lane · 6 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- Grok 4.20
- 34.2
- Unranked · 2 rankable rows
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Grok 4.20
- Not ranked
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Grok 4.20
- Not ranked
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Grok 4.20
- 34.6
- #43/48
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Grok 4.20
- Not ranked
- Laguna XS.2
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Grok 4.20 | Laguna XS.2 | Basis | Reading |
|---|---|---|---|---|
| Coding | 28.2Supported · #141/151 | 24.7Supported · #148/151 | Like-for-likeBenchAlign lane · 6 vs 6 public rows | Grok 4.20 leads · intervals overlap |
| Agentic | 26.7Supported · #145/152 | 22.4Estimated · #149/152 | Directional onlyBenchAlign lane · 4 vs 2 public rows | Directional only |
| Knowledge | 49.4Supported · #85/183 | 17.7Estimated · #183/183 | Directional onlyBenchAlign lane · 6 vs 2 public rows | Directional only |
| Reasoning | 34.2Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 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 | 34.6#43/48 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Instruction following | 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, 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.
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.
MMLU-Pro (Vals)
Knowledge
LiveCodeBench (Vals)
Coding
Terminal-Bench 2.0
Agentic
SWE-bench Verified
Coding
SWE-bench Pro
Coding
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
- Grok 4.20
- $0.005
- Fits in one request
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
Laguna XS.2 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Grok 4.20
- $0.118
- Fits in one request
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
Laguna XS.2 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Grok 4.20
- $0.5
- Fits in one request
- Cached input priced at the published list-input rate
- Laguna XS.2
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Grok 4.20 has no published cached-input rate, so cached tokens use its listed input rate. Laguna XS.2 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.
SpecificationGrok 4.20Laguna XS.2
Context window
Maximum documented context; output-token limits may be lower.
Grok 4.20
2M
Laguna XS.2
256K
API model ID
Grok 4.20
Not sourced
Laguna XS.2
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Grok 4.20
Not published
Laguna XS.2
No comparable hosted API rate
Documented inputs
Grok 4.20
Not sourced
Laguna XS.2
Not sourced
Documented outputs
Grok 4.20
Not sourced
Laguna XS.2
Not sourced
Provider availability
Grok 4.20
Not sourced
Laguna XS.2
Not sourced
Reasoning profile
Grok 4.20
Reasoning
Laguna XS.2
Reasoning
Weight access
Grok 4.20
Proprietary
Laguna XS.2
Open Weight
License
Grok 4.20
Proprietary
Laguna XS.2
Open Weight
Release date
Grok 4.20
2026-03-10
Laguna XS.2
2026-04-28
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
- Grok 4.20 has the higher public score, 67.13 versus 1.53, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Grok 4.20 has the larger documented window (2M).
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 evidence25 rows
Agentic
Terminal-Bench 2.0
Grok 4.2047.1%SourceLaguna XS.235.7%SourceGrok 4.20 leads this result
DeepSearchQA
Grok 4.2062.8%SourceLaguna XS.2—Not directly comparable
Gert Labs
Grok 4.2038.36%SourceLaguna XS.2—Not directly comparable
Terminal-Bench 2.1 (Vals)
Grok 4.2044.2%SourceLaguna XS.225.8%SourceGrok 4.20 leads this result
Coding
LiveCodeBench Pro
Grok 4.2074.2%SourceLaguna XS.2—Not directly comparable
SWE-bench Verified
Grok 4.2076.7%SourceLaguna XS.269.9%SourceGrok 4.20 leads this result
SWE-bench Pro
Grok 4.2051.8%SourceLaguna XS.246.3%SourceGrok 4.20 leads this result
Vibe Code Bench
Grok 4.204.06%SourceLaguna XS.2—Not directly comparable
LiveCodeBench (Vals)
Grok 4.2084.3%SourceLaguna XS.267.8%SourceGrok 4.20 leads this result
SWE-bench (Vals)
Grok 4.2072.2%SourceLaguna XS.255.2%SourceGrok 4.20 leads this result
SWE Multilingual
Grok 4.20—Laguna XS.257.7%SourceNot directly comparable
Terminal-Bench 2.0
Grok 4.20—Laguna XS.235.7%SourceNot directly comparable
Reasoning
Knowledge
GPQA-D
Grok 4.2088.5%SourceLaguna XS.2—Not directly comparable
HLE w/o tools
Grok 4.2031.6%SourceLaguna XS.2—Not directly comparable
HealthBench Hard
Grok 4.2020.3%SourceLaguna XS.2—Not directly comparable
MedXpertQA (Text)
Grok 4.2050.2%SourceLaguna XS.2—Not directly comparable
GPQA Diamond (Vals)
Grok 4.2088.6%SourceLaguna XS.255.1%SourceGrok 4.20 leads this result
MMLU-Pro (Vals)
Grok 4.2086.3%SourceLaguna XS.269.1%SourceGrok 4.20 leads this result
Multimodal
MMMU-Pro
Grok 4.2075.2%SourceLaguna XS.2—Not directly comparable
CharXiv
Grok 4.2060.9%SourceLaguna XS.2—Not directly comparable
ERQA
Grok 4.2054.1%SourceLaguna XS.2—Not directly comparable
SimpleVQA
Grok 4.2057.4%SourceLaguna XS.2—Not directly comparable
MedXpertQA (MM)
Grok 4.2065.8%SourceLaguna XS.2—Not directly comparable
Frequently asked questions
Which is better, Grok 4.20 or Laguna XS.2?
Grok 4.20 has the higher public score, 67.13 versus 1.53, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Grok 4.20 or Laguna XS.2?
Grok 4.20 leads the public coding lane, 28.2 to 24.7, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Grok 4.20 or Laguna XS.2?
Grok 4.20 scores higher for agentic tasks on the public lane, 26.7 to 22.4. Laguna XS.2 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, Grok 4.20 or Laguna XS.2?
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, Grok 4.20 or Laguna XS.2?
Grok 4.20 has the larger documented context window: 2M, compared with 256K.
Related comparisons
Last updated September 10, 2026
Watch Grok 4.20 vs Laguna XS.2
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.