The question every team is asking: Which model should I use for my coding agent? The answer depends on your constraints — cost, deployment, license, and task type. Here’s the data you need to decide.
The contenders
| Model | Total Params | Active Params | Context | License | Launch |
|---|---|---|---|---|---|
| DeepSeek V4-Flash | 284B | 13B | 1M | MIT | Jul 31, 2026 |
| DeepSeek V4-Pro | 1.6T | 49B | 1M | MIT | Aug 2026 (expected) |
| GPT-5.6 Sol | Undisclosed | Est. 3-4T | 256K | Proprietary | 2026 |
| Claude Opus 4.6 | Undisclosed | Est. 300-400B | 200K | Proprietary | 2026 |
| Kimi K3 | 2.8T | 104B | 1M | Custom (open weights) | Jul 16, 2026 |
| GLM-5.2 | 744B | 744B | 1M | Apache 2.0 | Jun 2026 |
Agent benchmarks: Head-to-head
| Benchmark | V4-Flash | V4-Pro (prev.) | Claude 4.6 | GLM-5.2 | Winner |
|---|---|---|---|---|---|
| Terminal Bench 2.1 | 82.7 | 67.9 | 85.0 | ~75 | Claude |
| DeepSWE | 54.4 | 7.3 | — | — | V4-Flash |
| Cybergym | 76.7 | — | — | — | V4-Flash |
| Toolathlon | 70.3 | — | — | — | V4-Flash |
| NL2Repo | 54.2 | — | — | — | V4-Flash |
Note: GPT-5.6 Sol and Kimi K3 haven’t published comparable agent benchmark scores. The comparison is limited to models with publicly available data.
Cost comparison: Price per million tokens
| Model | Input (per 1M) | Output (per 1M) | Cost vs V4-Flash |
|---|---|---|---|
| V4-Flash | $0.28 | $1.10 | 1× |
| GPT-4o | $2.50 | $10.00 | ~9× |
| GPT-5.6 Sol | $5.00+ | $20.00+ | ~18× |
| Claude Opus 4.6 | $15.00 | $75.00 | ~54× |
| Claude Sonnet 4 | $3.00 | $15.00 | ~12× |
| Kimi K3 (API) | $1.50 | $6.00 | ~5× |
| GLM-5.2 (API) | $1.00 | $4.00 | ~4× |
For a coding agent that averages 50 model calls per task at 10K input tokens each:
| Model | Cost per task | Cost per 100 tasks/day | Monthly cost |
|---|---|---|---|
| V4-Flash | $0.14 | $14 | $420 |
| GPT-4o | $1.25 | $125 | $3,750 |
| Claude Opus 4.6 | $7.50 | $750 | $22,500 |
| Kimi K3 | $0.75 | $75 | $2,250 |
Deployment flexibility
| Model | Self-host? | Hardware requirement | API available? |
|---|---|---|---|
| V4-Flash | Yes (MIT) | 13B active — single GPU feasible | Yes |
| V4-Pro | Yes (MIT) | 49B active — multi-GPU | Yes (soon) |
| GPT-5.6 | No | — | Yes |
| Claude Opus 4.6 | No | — | Yes |
| Kimi K3 | Yes (custom license) | 104B active — significant hardware | Yes |
| GLM-5.2 | Yes (Apache 2.0) | 744B dense — major hardware | Yes |
V4-Flash is the only frontier-class agent model that can realistically run on a single GPU in a self-hosted deployment. The 13B active parameter count makes it accessible to teams that can’t or won’t provision a multi-GPU cluster.
Decision framework
Choose V4-Flash if:
- You need the lowest cost per agent task
- You want to self-host on modest hardware
- MIT license is a requirement (no legal review needed)
- Your agent tasks are well-defined and bounded
- You’re running agents at high volume (100+ tasks/day)
Choose GPT-5.6 or Claude Opus if:
- You need the absolute best performance on complex reasoning
- Cost is not a primary constraint
- You need world knowledge beyond the training cutoff
- Your tasks require very large single-response code generation
Choose Kimi K3 if:
- You need open weights (not just open source)
- You have the hardware for 104B active parameters
- Multimodal capability is required (K3 has native multimodal)
- The custom license terms are acceptable for your use case
Choose GLM-5.2 if:
- You need Apache 2.0 licensing specifically
- You’re in the Chinese AI ecosystem and want domestic support
- You have the hardware for a 744B dense model
Wait for V4-Pro if:
- You need frontier reasoning but can’t afford Claude Opus pricing
- You have multi-GPU hardware available
- You can wait until August 2026
The verdict
For most coding teams, the answer is clear: start with V4-Flash. At 1/10 the cost of GPT-4o with agent scores within 2-3 points of Claude Opus, the economics are decisive. Use frontier models for the 10-20% of tasks that need the extra capability, and let V4-Flash handle the rest.
The only reason to wait is if you’re evaluating V4-Pro — but there’s no reason not to start evaluating V4-Flash today. The API is live, the MIT license is clear, and the cost is negligible.
For the methodology behind these numbers — how each benchmark is constructed and what the scores actually measure — see the architecture and benchmark deep dive. For how this reshapes the wider AI coding market, the ecosystem analysis covers who wins and who faces pressure. The full launch briefing ties the whole picture together.
You’ve seen the numbers. Now see the model in action.
V4-Flash vs GPT-5.6 vs Claude — the comparison is academic until you run it on your own code. MonkeyCode gives you 30M free tokens/day to do exactly that.