Agentic
Not comparable- Qwen3.8-Flash-Next
- 58.5
- Estimated · #27/152
- Sakana Namazu
- Not ranked
- Basis
- BenchAlign lane · 6 vs 0 public rows
- Reading
- Not comparable
Qwen3.8-Flash-Next vs Sakana Namazu
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.
Long documents
Prompts that approach the documented context limit
Qwen3.8-Flash-Next
Qwen3.8-Flash-Next has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Sakana Namazu 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
Sakana Namazu is not ranked on the public lane for agentic, so no winner is named for agentic.
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.
24 Qwen3.8-Flash-Next only
- Shared results
- 0
- Qwen3.8-Flash-Next only
- 24
- Sakana Namazu 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- Qwen3.8-Flash-Next
- 57.9
- Supported · #30/151
- Sakana Namazu
- Not ranked
- Basis
- BenchAlign lane · 5 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Qwen3.8-Flash-Next
- 75.8
- Unranked · 2 rankable rows
- Sakana Namazu
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Qwen3.8-Flash-Next
- 55.5
- Supported · #53/183
- Sakana Namazu
- Not ranked
- Basis
- BenchAlign lane · 4 vs 0 public rows
- Reading
- Not comparable
Math
Not comparable- Qwen3.8-Flash-Next
- Not ranked
- Sakana Namazu
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Qwen3.8-Flash-Next
- Not ranked
- Sakana Namazu
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Qwen3.8-Flash-Next
- 83.3
- #7/48
- Sakana Namazu
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Qwen3.8-Flash-Next
- 88.0
- #32/123
- Sakana Namazu
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | Qwen3.8-Flash-Next | Sakana Namazu | Basis | Reading |
|---|---|---|---|---|
| Agentic | 58.5Estimated · #27/152 | Not ranked | Not comparableBenchAlign lane · 6 vs 0 public rows | Not comparable |
| Coding | 57.9Supported · #30/151 | Not ranked | Not comparableBenchAlign lane · 5 vs 0 public rows | Not comparable |
| Reasoning | 75.8Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 55.5Supported · #53/183 | Not ranked | Not comparableBenchAlign lane · 4 vs 0 public 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 | 83.3#7/48 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Instruction following | 88.0#32/123 | Not ranked | Not comparableProvisional lane · 1 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
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
- Sakana Namazu
- $0.00295
- Fits in one request
Qwen3.8-Flash-Next has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
- Sakana Namazu
- $0.0595
- Fits in one request
Qwen3.8-Flash-Next has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Qwen3.8-Flash-Next
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
- Sakana Namazu
- $0.089
- Fits in one request
Qwen3.8-Flash-Next 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.
SpecificationQwen3.8-Flash-NextSakana Namazu
Context window
Maximum documented context; output-token limits may be lower.
Qwen3.8-Flash-Next
Sakana Namazu
API model ID
Qwen3.8-Flash-Next
Qwen/Qwen3.8-Flash-Next
Qwen3.8-Flash-Next model cardSakana Namazu
sakana-namazu
Sakana AI console pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Qwen3.8-Flash-Next
No comparable hosted API rate
Qwen3.8-Flash-Next model cardSakana Namazu
$0.15 per 1M cached input tokens
Sakana AI console pricingDocumented inputs
Qwen3.8-Flash-Next
Not sourced
Sakana Namazu
Not sourced
Documented outputs
Qwen3.8-Flash-Next
Not sourced
Sakana Namazu
Not sourced
Provider availability
Qwen3.8-Flash-Next
Not sourced
Sakana Namazu
Not sourced
Reasoning profile
Qwen3.8-Flash-Next
Reasoning
Sakana Namazu
Reasoning
Weight access
Qwen3.8-Flash-Next
Open Weight
Sakana Namazu
Proprietary
License
Qwen3.8-Flash-Next
Open Weight
Sakana Namazu
Proprietary
Release date
Qwen3.8-Flash-Next
2026-08-26
Sakana Namazu
2026-08-03
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
- Qwen3.8-Flash-Next has the larger documented window (262K).
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 evidence24 rows
Agentic
CoWorkBench
Qwen3.8-Flash-Next73.9%SourceSakana Namazu—Not directly comparable
JobBench
Qwen3.8-Flash-Next55.7%SourceSakana Namazu—Not directly comparable
Agents' Last Exam
Qwen3.8-Flash-Next51.2%SourceSakana Namazu—Not directly comparable
Toolathlon-Verified
Qwen3.8-Flash-Next73.5%SourceSakana Namazu—Not directly comparable
AndroidWorld
Qwen3.8-Flash-Next84.5%SourceSakana Namazu—Not directly comparable
OSWorld 2.0
Qwen3.8-Flash-Next19.4%SourceSakana Namazu—Not directly comparable
Coding
SWE-bench Pro
Qwen3.8-Flash-Next62.5%SourceSakana Namazu—Not directly comparable
SWE Multilingual
Qwen3.8-Flash-Next81%SourceSakana Namazu—Not directly comparable
NL2Repo
Qwen3.8-Flash-Next48.1%SourceSakana Namazu—Not directly comparable
DeepSWE
Qwen3.8-Flash-Next58.7%SourceSakana Namazu—Not directly comparable
LiveCodeBench v6
Qwen3.8-Flash-Next91.9%SourceSakana Namazu—Not directly comparable
Knowledge
Multimodal
Vision2Web
Qwen3.8-Flash-Next64.0%SourceSakana Namazu—Not directly comparable
ERQA
Qwen3.8-Flash-Next72.3%SourceSakana Namazu—Not directly comparable
LVBench
Qwen3.8-Flash-Next76.6%SourceSakana Namazu—Not directly comparable
RealWorldQA
Qwen3.8-Flash-Next88.5%SourceSakana Namazu—Not directly comparable
MathVision
Qwen3.8-Flash-Next90.6%SourceSakana Namazu—Not directly comparable
MathVision w/ Python
Qwen3.8-Flash-Next95.7%SourceSakana Namazu—Not directly comparable
CharXiv w/o tools
Qwen3.8-Flash-Next84.6%SourceSakana Namazu—Not directly comparable
CharXiv
Qwen3.8-Flash-Next90.6%SourceSakana Namazu—Not directly comparable
Instruction following
IFBench
Qwen3.8-Flash-Next81.3%SourceSakana Namazu—Not directly comparable
Frequently asked questions
Which is better, Qwen3.8-Flash-Next or Sakana Namazu?
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, Qwen3.8-Flash-Next or Sakana Namazu?
Sakana Namazu is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Qwen3.8-Flash-Next or Sakana Namazu?
Sakana Namazu is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Qwen3.8-Flash-Next or Sakana Namazu?
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, Qwen3.8-Flash-Next or Sakana Namazu?
Qwen3.8-Flash-Next has the larger documented context window: 262K, compared with 256K.
Related comparisons
Last updated September 10, 2026
Watch Qwen3.8-Flash-Next vs Sakana Namazu
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.