Agentic
Not comparable- Claude Sonnet 4.6
- 44.0
- Supported · #93/152
- Deepgram Flux
- Not ranked
- Basis
- BenchAlign lane · 8 vs 0 public rows
- Reading
- Not comparable
Claude Sonnet 4.6 vs Deepgram Flux
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
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
Deepgram Flux is 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
Deepgram Flux is not ranked on the public lane for agentic, so no winner is named for agentic.
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Deepgram Flux has no comparable published API token rate.
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.
25 Claude Sonnet 4.6 only
- Shared results
- 0
- Claude Sonnet 4.6 only
- 25
- Deepgram Flux only
- 0
- 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- Claude Sonnet 4.6
- 52.2
- Supported · #48/151
- Deepgram Flux
- Not ranked
- Basis
- BenchAlign lane · 8 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Sonnet 4.6
- 67.9
- Unranked · 2 rankable rows
- Deepgram Flux
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Sonnet 4.6
- 55.8
- Supported · #51/183
- Deepgram Flux
- Not ranked
- Basis
- BenchAlign lane · 6 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 4.6
- 49.0
- Unranked · 2 rankable rows
- Deepgram Flux
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 4.6
- Not ranked
- Deepgram Flux
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Sonnet 4.6
- 54.1
- #33/48
- Deepgram Flux
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 4.6
- 48.2
- #86/123
- Deepgram Flux
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 4.6 | Deepgram Flux | Basis | Reading |
|---|---|---|---|---|
| Agentic | 44.0Supported · #93/152 | Not ranked | Not comparableBenchAlign lane · 8 vs 0 public rows | Not comparable |
| Coding | 52.2Supported · #48/151 | Not ranked | Not comparableBenchAlign lane · 8 vs 0 public rows | Not comparable |
| Reasoning | 67.9Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 55.8Supported · #51/183 | Not ranked | Not comparableBenchAlign lane · 6 vs 0 public rows | Not comparable |
| Math | 49.0Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 54.1#33/48 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Instruction following | 48.2#86/123 | 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
- Claude Sonnet 4.6
- $0.0105
- Fits in one request
- Deepgram Flux
- API rate not published
- Fit state unavailable
Deepgram Flux has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Claude Sonnet 4.6
- $0.195
- Fits in one request
- Deepgram Flux
- API rate not published
- Fit state unavailable
Deepgram Flux has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Sonnet 4.6
- $0.81
- Does not fit in one request
- Cached input priced at the published list-input rate
- Deepgram Flux
- API rate not published
- Fit state unavailable
- Cached-input rate unavailable
Claude Sonnet 4.6 does not fit this workload in one request. Claude Sonnet 4.6 has no published cached-input rate, so cached tokens use its listed input rate. Deepgram Flux 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.
SpecificationClaude Sonnet 4.6Deepgram Flux
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 4.6
200K
Deepgram Flux
N/A
API model ID
Claude Sonnet 4.6
Not sourced
Deepgram Flux
flux-general-en
Deepgram model documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 4.6
Not published
Deepgram Flux
No comparable hosted API rate
Deepgram model documentationDocumented inputs
Claude Sonnet 4.6
Not sourced
Deepgram Flux
Not sourced
Documented outputs
Claude Sonnet 4.6
Not sourced
Deepgram Flux
Not sourced
Provider availability
Claude Sonnet 4.6
Not sourced
Deepgram Flux
Not sourced
Reasoning profile
Claude Sonnet 4.6
Non-Reasoning
Deepgram Flux
Non-Reasoning
Weight access
Claude Sonnet 4.6
Proprietary
Deepgram Flux
Proprietary
License
Claude Sonnet 4.6
Proprietary
Deepgram Flux
Proprietary
Release date
Claude Sonnet 4.6
2026-02-01
Deepgram Flux
2025-10-02
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
- 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 evidence25 rows
Agentic
Terminal-Bench 2.0
Claude Sonnet 4.659.1%SourceDeepgram Flux—Not directly comparable
OSWorld-Verified
Claude Sonnet 4.672.1%SourceDeepgram Flux—Not directly comparable
Claw-Eval
Claude Sonnet 4.667.8%SourceDeepgram Flux—Not directly comparable
CyberGym
Claude Sonnet 4.665.2%SourceDeepgram Flux—Not directly comparable
Gert Labs
Claude Sonnet 4.662.92%SourceDeepgram Flux—Not directly comparable
OSWorld 2.0
Claude Sonnet 4.68.3%SourceDeepgram Flux—Not directly comparable
JobBench
Claude Sonnet 4.636.9%SourceDeepgram Flux—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 4.657.3%SourceDeepgram Flux—Not directly comparable
Coding
SWE-bench Verified
Claude Sonnet 4.679.6%SourceDeepgram Flux—Not directly comparable
SWE-Rebench
Claude Sonnet 4.660.7%SourceDeepgram Flux—Not directly comparable
React Native Evals
Claude Sonnet 4.680.6%SourceDeepgram Flux—Not directly comparable
Vibe Code Bench
Claude Sonnet 4.651.48%SourceDeepgram Flux—Not directly comparable
cursorBench31
Claude Sonnet 4.648.8%SourceDeepgram Flux—Not directly comparable
FrontierCode 1.1 Main
Claude Sonnet 4.624.3%SourceDeepgram Flux—Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 4.682.1%SourceDeepgram Flux—Not directly comparable
SWE-bench (Vals)
Claude Sonnet 4.677.4%SourceDeepgram Flux—Not directly comparable
Knowledge
GPQA
Claude Sonnet 4.689.9%SourceDeepgram Flux—Not directly comparable
SuperGPQA
Claude Sonnet 4.695%SourceDeepgram Flux—Not directly comparable
MMLU-Pro
Claude Sonnet 4.679.2%SourceDeepgram Flux—Not directly comparable
HLE
Claude Sonnet 4.649%SourceDeepgram Flux—Not directly comparable
GPQA Diamond (Vals)
Claude Sonnet 4.685.6%SourceDeepgram Flux—Not directly comparable
MMLU-Pro (Vals)
Claude Sonnet 4.687.3%SourceDeepgram Flux—Not directly comparable
Math
Multimodal
CharXiv
Claude Sonnet 4.677.4%SourceDeepgram Flux—Not directly comparable
Frequently asked questions
Which is better, Claude Sonnet 4.6 or Deepgram Flux?
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, Claude Sonnet 4.6 or Deepgram Flux?
Deepgram Flux is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Claude Sonnet 4.6 or Deepgram Flux?
Deepgram Flux is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude Sonnet 4.6 or Deepgram Flux?
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, Claude Sonnet 4.6 or Deepgram Flux?
A complete documented context-window comparison is not available.
Related comparisons
Last updated September 10, 2026
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