Agentic
Not comparable- Quasar 438B
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- BenchAlign lane · 1 vs 4 public rows
- Reading
- Not comparable
Quasar 438B vs Solar Pro 4
Updated September 10, 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.
2 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.
Long documents
Prompts that approach the documented context limit
Quasar 438B
Quasar 438B has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Solar Pro 4
Solar Pro 4 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
Solar Pro 4
Solar Pro 4 has the lower estimated token cost for this stated workload. Quasar 438B 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
Solar Pro 4
Solar Pro 4 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
Quasar 438B and Solar Pro 4 are 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
Quasar 438B and Solar Pro 4 are not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 Shared
8 Solar Pro 4 only
- Shared results
- 2
- Quasar 438B only
- 0
- Solar Pro 4 only
- 8
- Like-for-like categories
- 0 / 8
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
Not comparable- Quasar 438B
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- BenchAlign lane · 1 vs 3 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Quasar 438B
- 73.4
- Unranked · 2 rankable rows
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Quasar 438B
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- BenchAlign lane · 0 vs 2 public rows
- Reading
- Not comparable
Math
Not comparable- Quasar 438B
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Quasar 438B
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Quasar 438B
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Quasar 438B
- Not ranked
- Solar Pro 4
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Quasar 438B | Solar Pro 4 | Basis | Reading |
|---|---|---|---|---|
| Agentic | Not ranked | Not ranked | Not comparableBenchAlign lane · 1 vs 4 public rows | Not comparable |
| Coding | Not ranked | Not ranked | Not comparableBenchAlign lane · 1 vs 3 public rows | Not comparable |
| Reasoning | 73.4Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | Not ranked | Not ranked | Not comparableBenchAlign lane · 0 vs 2 public rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 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 |
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
- Quasar 438B
- $0.0015
- Fits in one request
- Solar Pro 4
- $0.0009
- Fits in one request
Solar Pro 4 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Quasar 438B
- $0.0354
- Fits in one request
- Solar Pro 4
- $0.0186
- Fits in one request
Solar Pro 4 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Quasar 438B
- $0.15
- Fits in one request
- Cached input priced at the published list-input rate
- Solar Pro 4
- $0.03
- Fits in one request
Solar Pro 4 has the lower modeled cost
Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationQuasar 438BSolar Pro 4
Context window
Maximum documented context; output-token limits may be lower.
Quasar 438B
Solar Pro 4
API model ID
Quasar 438B
quasar-438b
CompactifAI Quasar 438B documentationSolar Pro 4
solar-pro4
Upstage Solar Pro 4 launch postCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Quasar 438B
Not published
CompactifAI API pricingSolar Pro 4
$0.06 per 1M cached input tokens
Upstage Solar Pro 4 launch postDocumented inputs
Quasar 438B
Not sourced
Solar Pro 4
Not sourced
Documented outputs
Quasar 438B
Not sourced
Solar Pro 4
Not sourced
Provider availability
Quasar 438B
Generally Available · CompactifAI API
CompactifAI Quasar 438B documentationSolar Pro 4
Not sourced
Reasoning profile
Quasar 438B
Reasoning
Solar Pro 4
Reasoning
Weight access
Quasar 438B
Proprietary
Solar Pro 4
Proprietary
License
Quasar 438B
Proprietary
Solar Pro 4
Proprietary
Release date
Quasar 438B
2026-09-02
Solar Pro 4
2026-08-11
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: $0.0354 vs $0.0186. Cache-heavy agent loop: $0.15 vs $0.03.
- Context tradeoff
- Quasar 438B has the larger documented window (1M).
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 evidence10 rows
Frequently asked questions
Which is better, Quasar 438B or Solar Pro 4?
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, Quasar 438B or Solar Pro 4?
Quasar 438B and Solar Pro 4 are not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Quasar 438B or Solar Pro 4?
Quasar 438B and Solar Pro 4 are not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Quasar 438B or Solar Pro 4?
For the stated presets, chat costs $0.0015 on Quasar 438B and $0.0009 on Solar Pro 4; repository review costs $0.0354 and $0.0186; the cache-heavy agent loop costs $0.15 and $0.03. Quasar 438B has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Quasar 438B or Solar Pro 4?
Quasar 438B has the larger documented context window: 1M, compared with 512K.
Related comparisons
Last updated September 10, 2026
Watch Quasar 438B vs Solar Pro 4
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.