| Signal | ALLaM 34B | Delta | SWE-1.5 |
|---|---|---|---|
Capabilities | 17 | -33 | |
Pricing | 100 | -- | |
Context window size | 52 | +52 | |
Recency | 83 | -1 | |
Output Capacity | 60 | +40 | |
| Overall Result | 2 wins | of 5 | 2 wins |
Score History
40
current score
Tied
right now
40
current score
HUMAIN
Windsurf
| Metric | ALLaM 34B | SWE-1.5 | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #304 | #302 | SWE-1.5 |
| Quality Rank | #304 | #302 | SWE-1.5 |
| Adoption Rank | #304 | #302 | SWE-1.5 |
| Parameters | 34B | -- | -- |
| Context Window | 4K | -- | -- |
| Pricing | Free | Free | -- |
| Signal Scores | |||
| Capabilities | 17 | 50 | SWE-1.5 |
| Pricing | 100 | 100 | ALLaM 34B |
| Context window size | 52 | 0 | ALLaM 34B |
| Recency | 83 | 84 | SWE-1.5 |
| Output Capacity | 60 | 20 | ALLaM 34B |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 40/100 (rank #304), placing it in the top -4% of all 290 models tracked.
Scores 40/100 (rank #302), placing it in the top -4% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Both models are priced similarly, so the decision comes down to quality and features rather than cost.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. ALLaM 34B also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (4K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.00/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (40/100) correlates with better nuance, coherence, and style in long-form content
ALLaM 34B and SWE-1.5 are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
ALLaM 34B
Marginally better benchmark scores; both are excellent
Best for Cost
ALLaM 34B
0% lower pricing; better value at scale
Best for Reliability
ALLaM 34B
Higher uptime and faster response speeds
Best for Prototyping
ALLaM 34B
Stronger community support and better developer experience
Best for Production
ALLaM 34B
Wider enterprise adoption and proven at scale
by HUMAIN
| Capability | ALLaM 34B | SWE-1.5 |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Mode | ||
| Reasoningdiffers | ||
| Web Search | ||
| Image Output |
HUMAIN
Windsurf
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | ALLaM 34B | SWE-1.5 |
|---|---|---|
| Context Window | 4K | -- |
| Max Output Tokens | 4,096 | -- |
| Open Source | No | No |
| Created | Aug 25, 2025 | Sep 1, 2025 |